The document discusses sequence to sequence learning for chatbots. It provides an overview of the network architecture, including encoding the input sequence and decoding the output sequence. LSTM is used to model language. The loss function is cross entropy loss. Improvement techniques discussed include attention mechanism to focus on relevant parts of the input, and beam search to find higher probability full sequences during decoding.
This document discusses the history and development of chatbots from early rule-based systems to modern deep learning approaches. It defines chatbots as programs that simulate human conversation and outlines some of the early systems from the 1960s to 2000s that were rule-based. It then introduces natural language processing and machine learning, focusing on neural network models and word embeddings. Finally, it provides a brief overview of how to build a neural conversational model using a sequence to sequence approach and train it on a dialog corpus, noting some caveats like needing large amounts of data and compute resources.
The document discusses chat bots and their potential future uses. It notes that apps have already created millions of jobs and bots may be the next step. Bots can perform automated tasks like answering questions or taking orders through messaging apps. Currently, people are using messaging apps more than social networks. The document outlines different types of bots including those that operate through rules-based programming and more advanced bots using machine learning that can understand language. It provides examples of potential bots and services to build bots. It concludes by recommending Cisco leverage chat bots for quick answers, analyzing Facebook messages, and developing future uses in tech support, sales, and communications between companies' bots.
How do Chatbots Work? A Guide to Chatbot ArchitectureMaruti Techlabs
A chatbot is a program that can have conversations with humans without human assistance. There are two types of chatbots: rule-based chatbots that are limited to their programming, and AI-based chatbots that can understand open-ended queries using machine learning. Chatbots work through question and answering systems, natural language processing to understand context, and by adopting classification methods like pattern matching, algorithms, and artificial neural networks.
This document outlines a project to design and develop a Sugar CRM bot using Artificial Intelligence Markup Language (AIML). The objective is to create a bot that can answer questions about Sugar CRM. It will be implemented as both a desktop and web application using programming languages like AIML, Python, and Adobe Flex. An automatic AIML generation tool will also be developed to ease the creation of AIML files. The source code for the project is available online for checkout and demonstration.
This document discusses how social media can be used to help businesses in several ways:
1. Social media can drive brand awareness, relevance, and value by amplifying messaging to more people.
2. It provides ways to increase sales through new customer acquisition, increased transactions, and product exposure.
3. It allows for improved customer support, public relations, loyalty, and intelligence gathering.
4. User-generated content like reviews provide a global platform for sharing opinions that can influence decisions. Hashtags help group posts by topic to understand sentiment.
Deep Learning approaches for Hate speech detection. In this work we used the two deep learning approaches DCNN and MLP two separate classifier on four publicly available datasets.
Chatbots are computer programs designed to simulate conversation with humans over the Internet. Examples include Cortana, Siri, and Eliza, the first chatbot created by Joseph Weizenbaum. Chatbots provide information quickly and efficiently for productivity or entertainment, fueling conversations to avoid loneliness. They are trained using large datasets of conversation logs to understand language and connect questions to answers. While chatbots reduce costs and can handle many users at once, they have limitations in complex conversations and understanding intent. Future chatbots may become more specialized and useful in applications like e-commerce, travel, and events.
The document discusses and compares three open source platforms for building chatbots: Dialogflow, Snatchbot, and Chatfuel. Dialogflow is highlighted as having powerful machine learning and natural language processing capabilities. Snatchbot's visual editor allows for pre-defined templates but has less robust NLP than Dialogflow. Chatfuel provides contact history, customization, and third party integrations, but has limited NLP and support for complex conversations. Overall, Dialogflow is positioned as best for natural conversations while the others have more limitations.
This document discusses the history and development of chatbots from early rule-based systems to modern deep learning approaches. It defines chatbots as programs that simulate human conversation and outlines some of the early systems from the 1960s to 2000s that were rule-based. It then introduces natural language processing and machine learning, focusing on neural network models and word embeddings. Finally, it provides a brief overview of how to build a neural conversational model using a sequence to sequence approach and train it on a dialog corpus, noting some caveats like needing large amounts of data and compute resources.
The document discusses chat bots and their potential future uses. It notes that apps have already created millions of jobs and bots may be the next step. Bots can perform automated tasks like answering questions or taking orders through messaging apps. Currently, people are using messaging apps more than social networks. The document outlines different types of bots including those that operate through rules-based programming and more advanced bots using machine learning that can understand language. It provides examples of potential bots and services to build bots. It concludes by recommending Cisco leverage chat bots for quick answers, analyzing Facebook messages, and developing future uses in tech support, sales, and communications between companies' bots.
How do Chatbots Work? A Guide to Chatbot ArchitectureMaruti Techlabs
A chatbot is a program that can have conversations with humans without human assistance. There are two types of chatbots: rule-based chatbots that are limited to their programming, and AI-based chatbots that can understand open-ended queries using machine learning. Chatbots work through question and answering systems, natural language processing to understand context, and by adopting classification methods like pattern matching, algorithms, and artificial neural networks.
This document outlines a project to design and develop a Sugar CRM bot using Artificial Intelligence Markup Language (AIML). The objective is to create a bot that can answer questions about Sugar CRM. It will be implemented as both a desktop and web application using programming languages like AIML, Python, and Adobe Flex. An automatic AIML generation tool will also be developed to ease the creation of AIML files. The source code for the project is available online for checkout and demonstration.
This document discusses how social media can be used to help businesses in several ways:
1. Social media can drive brand awareness, relevance, and value by amplifying messaging to more people.
2. It provides ways to increase sales through new customer acquisition, increased transactions, and product exposure.
3. It allows for improved customer support, public relations, loyalty, and intelligence gathering.
4. User-generated content like reviews provide a global platform for sharing opinions that can influence decisions. Hashtags help group posts by topic to understand sentiment.
Deep Learning approaches for Hate speech detection. In this work we used the two deep learning approaches DCNN and MLP two separate classifier on four publicly available datasets.
Chatbots are computer programs designed to simulate conversation with humans over the Internet. Examples include Cortana, Siri, and Eliza, the first chatbot created by Joseph Weizenbaum. Chatbots provide information quickly and efficiently for productivity or entertainment, fueling conversations to avoid loneliness. They are trained using large datasets of conversation logs to understand language and connect questions to answers. While chatbots reduce costs and can handle many users at once, they have limitations in complex conversations and understanding intent. Future chatbots may become more specialized and useful in applications like e-commerce, travel, and events.
The document discusses and compares three open source platforms for building chatbots: Dialogflow, Snatchbot, and Chatfuel. Dialogflow is highlighted as having powerful machine learning and natural language processing capabilities. Snatchbot's visual editor allows for pre-defined templates but has less robust NLP than Dialogflow. Chatfuel provides contact history, customization, and third party integrations, but has limited NLP and support for complex conversations. Overall, Dialogflow is positioned as best for natural conversations while the others have more limitations.
Why Social Media Chat Bots Are the Future of Communication - DeckJan Rezab
Social media chat bots are the future of communication, if its WhatsApp, Facebook Messenger, Kik, Skype, or Telegram - you can use their bots and bot stores to easily access new services - easier you could ever do it with apps.
Using Machine Learning and Chatbots to handle 1st line Technical SupportBarbara Fusinska
This document discusses using machine learning and chatbots to handle first line technical support. It begins with an introduction to the speaker and agenda. It then provides an overview of what chatbots are, including definitions and examples. Common uses of chatbots for customer service and technical support are described. The typical architecture of a chatbot is outlined. Popular chatbot platforms are listed. An example use case of an "IT Crowd Answering Machine" chatbot is demonstrated. The document discusses using natural language processing and classification models to apply artificial intelligence to chatbots. It shows how this can be done using tools like LUIS and the Bot Framework. Challenges of training chatbots and correctly classifying user inputs are also mentioned.
The document discusses implementing chatbots using deep learning. It begins by defining what a chatbot is and listing some popular existing chatbots. It then describes two types of chatbot models - retrieval-based models which use predefined responses and generative models which continuously learn from conversations. The document focuses on implementing a retrieval-based model using the Ubuntu Dialog Corpus dataset and a dual encoder LSTM network model in TensorFlow. It outlines the preprocessing, model architecture, creating input functions, training, evaluating, and making predictions with the trained model.
This document provides an overview of chatbots including:
- Defining what chatbots are and why they are important for app developers.
- Discussing different approaches for chatbots such as replacing apps, greeting users for apps/websites, and using conversation as a means or end.
- Covering best practices for conversational UX, platforms to build chatbots, and why the presenter likes the API.ai platform.
Chatbot and Virtual AI Assistant Implementation in Natural Language Processing Shrutika Oswal
In this presentation, I have given a short overview of hot recent topics of research in artificial intelligence. These topics include Gaming, Expert System, Vision System, Speech Recognition, Handwriting Recognition, Intelligent Robots, Machine Learning, Deep Learning, Robotics, Reinforcement Learning, Internet of Things, Neuromorphic Computing, Computer Vision and most important NLP (Natural language Processing). Here I have mentioned different fields and components of NLP along with the steps of implementation. In the further part of the presentation, I have described the general structure of chatbot in NLP along with its implementation algorithm in python language. Also, I have given some informative descriptions, technologies, usage, and working of virtual AI assistants along with this I implemented one virtual assistant for laptop who will able to perform some interesting tasks.
Introduction to Named Entity RecognitionTomer Lieber
Named Entity Recognition (NER) is a common task in Natural Language Processing that aims to find and classify named entities in text, such as person names, organizations, and locations, into predefined categories. NER can be used for applications like machine translation, information retrieval, and question answering. Traditional approaches to NER involve feature extraction and training statistical or machine learning models on features, while current state-of-the-art methods use deep learning models like LSTMs combined with word embeddings. NER performance is typically evaluated using the F1 score, which balances precision and recall of named entity detection.
Machine learning is the intersection of statistics and computer science that allows systems to answer questions by learning from available data rather than through explicit programming. A machine learning model is trained on sample data to learn patterns and make predictions on new data. The accuracy of a machine learning model depends on the quality and quantity of training data as well as the robustness of the model. Machine learning is used in applications like speech recognition, fraud detection, spam filtering, search engines, and facial recognition. More data leads to stronger machine learning models that can tackle increasingly complex problems such as medical diagnosis, game playing, and self-driving vehicles.
AI Agent and Chatbot Trends For EnterprisesTeewee Ang
This document discusses the growing trend of chatbots and artificial intelligence assistants. It notes that major tech entrepreneurs like Mark Zuckerberg and Elon Musk have expressed interest in AI. While Musk sees AI as a potential threat, Zuckerberg wants to create an AI assistant for home use. The document outlines how chatbots use technologies like natural language processing and machine learning. It provides examples of chatbots being used in applications like customer service, human resources, and scheduling. In conclusion, the document predicts that AI assistant and chatbot applications will continue growing in both enterprise and consumer spaces.
This document provides an overview of chatbots and the growing chatbot ecosystem. It discusses why natural language interfaces are important, defines what a chatbot is, explores where chatbots are being used, outlines what capabilities chatbots have, and describes the growing platform and tools available for building chatbots. It emphasizes that while building basic chatbots is easy, creating truly useful chatbots requires serious thought and work.
This document provides an overview of representation learning techniques for natural language processing (NLP). It begins with introducing the speakers and objectives of the workshop, which is to provide a deep dive into state-of-the-art text representation techniques and how to apply them to solve NLP problems. The workshop covers four modules: 1) archaic techniques, 2) word vectors, 3) sentence/paragraph/document vectors, and 4) character vectors. It emphasizes that representation learning is key to NLP as it transforms raw text into a numeric form that machine learning models can understand.
NLP with Deep Learning Guest Lecture slides by Fatih Mehmet Güler, PragmaCraft. Includes my background on the subject, our projects, the NLP stages and the latest developments.
This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart
This document discusses natural language processing (NLP) and feature extraction. It explains that NLP can be used for applications like search, translation, and question answering. The document then discusses extracting features from text like paragraphs, sentences, words, parts of speech, entities, sentiment, topics, and assertions. Specific features discussed in more detail include frequency, relationships between words, language features, supervised machine learning, classifiers, encoding words, word vectors, and parse trees. Tools mentioned for NLP include Google Cloud NLP, Spacy, OpenNLP, and Stanford Core NLP.
Recommendation systems provide users with information they may be interested in based on their preferences and interests. They help address the problem of information overload by retrieving desired information for the user based on their preferences or those of similar users. The two main types of recommendation systems are personalized and non-personalized systems. Common techniques used include collaborative filtering, which finds users with similar tastes, and content-based filtering, which recommends items similar to those a user has liked based on item attributes.
This document describes the development of a chatbot application using Python to answer queries about a college. It discusses the existing system of students having to visit the college in person to ask questions, and the limitations thereof. The proposed chatbot system allows students to get college information by chatting with the bot through text. The document outlines the modules, design, and functioning of the chatbot, including its ability to understand natural language queries and provide relevant answers from its database. It concludes discussing the benefits of chatbots and potential for future improvements.
An overview of some key concepts of chatbots, with some do's and don'ts.
We will happily present the high-resolution version of this presentation, extended with additional detailed slides, and a clear explanation at your offices. Contact us for that.
Introduction to natural language processing (NLP)Alia Hamwi
The document provides an introduction to natural language processing (NLP). It defines NLP as a field of artificial intelligence devoted to creating computers that can use natural language as input and output. Some key NLP applications mentioned include data analysis of user-generated content, conversational agents, translation, classification, information retrieval, and summarization. The document also discusses various linguistic levels of analysis like phonology, morphology, syntax, and semantics that involve ambiguity challenges. Common NLP tasks like part-of-speech tagging, named entity recognition, parsing, and information extraction are described. Finally, the document outlines the typical steps in an NLP pipeline including data collection, text cleaning, preprocessing, feature engineering, modeling and evaluation.
This document summarizes an event organized by Pantech Solutions and the Institution of Electronics and Telecommunication (IETE) on the future of artificial intelligence. The event featured several presentations and demos on topics related to AI, including computer vision with deep learning, natural language processing, machine and deep learning, AI applications in various domains like medical, agriculture, autonomous vehicles, and brain-computer interfaces. It also discussed topics like machine learning, deep learning, AI safety concerns, and examples of AI applications in areas like search engines, social media, e-commerce, music and more. The agenda included presentations on object recognition with YOLO, brain enhancement with BCI technology, and a Python AI demo.
This document discusses character-based hybrid sentiment analysis. It begins by outlining reasons for performing sentiment analysis, such as determining if movie reviews are positive or negative. It then discusses challenges like informal language with misspellings and new words. Different neural network approaches are reviewed, including recurrent neural networks, convolutional neural networks, and hybrid CNN-RNN models. A novel approach is proposed that uses a character-based CNN to generate word embeddings, followed by a CNN to extract local features and an RNN to model long-distance dependencies, for the purpose of sentiment analysis.
Why Social Media Chat Bots Are the Future of Communication - DeckJan Rezab
Social media chat bots are the future of communication, if its WhatsApp, Facebook Messenger, Kik, Skype, or Telegram - you can use their bots and bot stores to easily access new services - easier you could ever do it with apps.
Using Machine Learning and Chatbots to handle 1st line Technical SupportBarbara Fusinska
This document discusses using machine learning and chatbots to handle first line technical support. It begins with an introduction to the speaker and agenda. It then provides an overview of what chatbots are, including definitions and examples. Common uses of chatbots for customer service and technical support are described. The typical architecture of a chatbot is outlined. Popular chatbot platforms are listed. An example use case of an "IT Crowd Answering Machine" chatbot is demonstrated. The document discusses using natural language processing and classification models to apply artificial intelligence to chatbots. It shows how this can be done using tools like LUIS and the Bot Framework. Challenges of training chatbots and correctly classifying user inputs are also mentioned.
The document discusses implementing chatbots using deep learning. It begins by defining what a chatbot is and listing some popular existing chatbots. It then describes two types of chatbot models - retrieval-based models which use predefined responses and generative models which continuously learn from conversations. The document focuses on implementing a retrieval-based model using the Ubuntu Dialog Corpus dataset and a dual encoder LSTM network model in TensorFlow. It outlines the preprocessing, model architecture, creating input functions, training, evaluating, and making predictions with the trained model.
This document provides an overview of chatbots including:
- Defining what chatbots are and why they are important for app developers.
- Discussing different approaches for chatbots such as replacing apps, greeting users for apps/websites, and using conversation as a means or end.
- Covering best practices for conversational UX, platforms to build chatbots, and why the presenter likes the API.ai platform.
Chatbot and Virtual AI Assistant Implementation in Natural Language Processing Shrutika Oswal
In this presentation, I have given a short overview of hot recent topics of research in artificial intelligence. These topics include Gaming, Expert System, Vision System, Speech Recognition, Handwriting Recognition, Intelligent Robots, Machine Learning, Deep Learning, Robotics, Reinforcement Learning, Internet of Things, Neuromorphic Computing, Computer Vision and most important NLP (Natural language Processing). Here I have mentioned different fields and components of NLP along with the steps of implementation. In the further part of the presentation, I have described the general structure of chatbot in NLP along with its implementation algorithm in python language. Also, I have given some informative descriptions, technologies, usage, and working of virtual AI assistants along with this I implemented one virtual assistant for laptop who will able to perform some interesting tasks.
Introduction to Named Entity RecognitionTomer Lieber
Named Entity Recognition (NER) is a common task in Natural Language Processing that aims to find and classify named entities in text, such as person names, organizations, and locations, into predefined categories. NER can be used for applications like machine translation, information retrieval, and question answering. Traditional approaches to NER involve feature extraction and training statistical or machine learning models on features, while current state-of-the-art methods use deep learning models like LSTMs combined with word embeddings. NER performance is typically evaluated using the F1 score, which balances precision and recall of named entity detection.
Machine learning is the intersection of statistics and computer science that allows systems to answer questions by learning from available data rather than through explicit programming. A machine learning model is trained on sample data to learn patterns and make predictions on new data. The accuracy of a machine learning model depends on the quality and quantity of training data as well as the robustness of the model. Machine learning is used in applications like speech recognition, fraud detection, spam filtering, search engines, and facial recognition. More data leads to stronger machine learning models that can tackle increasingly complex problems such as medical diagnosis, game playing, and self-driving vehicles.
AI Agent and Chatbot Trends For EnterprisesTeewee Ang
This document discusses the growing trend of chatbots and artificial intelligence assistants. It notes that major tech entrepreneurs like Mark Zuckerberg and Elon Musk have expressed interest in AI. While Musk sees AI as a potential threat, Zuckerberg wants to create an AI assistant for home use. The document outlines how chatbots use technologies like natural language processing and machine learning. It provides examples of chatbots being used in applications like customer service, human resources, and scheduling. In conclusion, the document predicts that AI assistant and chatbot applications will continue growing in both enterprise and consumer spaces.
This document provides an overview of chatbots and the growing chatbot ecosystem. It discusses why natural language interfaces are important, defines what a chatbot is, explores where chatbots are being used, outlines what capabilities chatbots have, and describes the growing platform and tools available for building chatbots. It emphasizes that while building basic chatbots is easy, creating truly useful chatbots requires serious thought and work.
This document provides an overview of representation learning techniques for natural language processing (NLP). It begins with introducing the speakers and objectives of the workshop, which is to provide a deep dive into state-of-the-art text representation techniques and how to apply them to solve NLP problems. The workshop covers four modules: 1) archaic techniques, 2) word vectors, 3) sentence/paragraph/document vectors, and 4) character vectors. It emphasizes that representation learning is key to NLP as it transforms raw text into a numeric form that machine learning models can understand.
NLP with Deep Learning Guest Lecture slides by Fatih Mehmet Güler, PragmaCraft. Includes my background on the subject, our projects, the NLP stages and the latest developments.
This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart
This document discusses natural language processing (NLP) and feature extraction. It explains that NLP can be used for applications like search, translation, and question answering. The document then discusses extracting features from text like paragraphs, sentences, words, parts of speech, entities, sentiment, topics, and assertions. Specific features discussed in more detail include frequency, relationships between words, language features, supervised machine learning, classifiers, encoding words, word vectors, and parse trees. Tools mentioned for NLP include Google Cloud NLP, Spacy, OpenNLP, and Stanford Core NLP.
Recommendation systems provide users with information they may be interested in based on their preferences and interests. They help address the problem of information overload by retrieving desired information for the user based on their preferences or those of similar users. The two main types of recommendation systems are personalized and non-personalized systems. Common techniques used include collaborative filtering, which finds users with similar tastes, and content-based filtering, which recommends items similar to those a user has liked based on item attributes.
This document describes the development of a chatbot application using Python to answer queries about a college. It discusses the existing system of students having to visit the college in person to ask questions, and the limitations thereof. The proposed chatbot system allows students to get college information by chatting with the bot through text. The document outlines the modules, design, and functioning of the chatbot, including its ability to understand natural language queries and provide relevant answers from its database. It concludes discussing the benefits of chatbots and potential for future improvements.
An overview of some key concepts of chatbots, with some do's and don'ts.
We will happily present the high-resolution version of this presentation, extended with additional detailed slides, and a clear explanation at your offices. Contact us for that.
Introduction to natural language processing (NLP)Alia Hamwi
The document provides an introduction to natural language processing (NLP). It defines NLP as a field of artificial intelligence devoted to creating computers that can use natural language as input and output. Some key NLP applications mentioned include data analysis of user-generated content, conversational agents, translation, classification, information retrieval, and summarization. The document also discusses various linguistic levels of analysis like phonology, morphology, syntax, and semantics that involve ambiguity challenges. Common NLP tasks like part-of-speech tagging, named entity recognition, parsing, and information extraction are described. Finally, the document outlines the typical steps in an NLP pipeline including data collection, text cleaning, preprocessing, feature engineering, modeling and evaluation.
This document summarizes an event organized by Pantech Solutions and the Institution of Electronics and Telecommunication (IETE) on the future of artificial intelligence. The event featured several presentations and demos on topics related to AI, including computer vision with deep learning, natural language processing, machine and deep learning, AI applications in various domains like medical, agriculture, autonomous vehicles, and brain-computer interfaces. It also discussed topics like machine learning, deep learning, AI safety concerns, and examples of AI applications in areas like search engines, social media, e-commerce, music and more. The agenda included presentations on object recognition with YOLO, brain enhancement with BCI technology, and a Python AI demo.
This document discusses character-based hybrid sentiment analysis. It begins by outlining reasons for performing sentiment analysis, such as determining if movie reviews are positive or negative. It then discusses challenges like informal language with misspellings and new words. Different neural network approaches are reviewed, including recurrent neural networks, convolutional neural networks, and hybrid CNN-RNN models. A novel approach is proposed that uses a character-based CNN to generate word embeddings, followed by a CNN to extract local features and an RNN to model long-distance dependencies, for the purpose of sentiment analysis.
Messaging apps have become more popular ways for people to communicate than social networks or phone calls. As a result, chatbots are growing in usefulness, especially for customer service tasks. There are different types of chatbots, from task-oriented bots to predictive, data-driven bots. Current chatbots have capabilities for intent recognition, entity recognition, and sentiment analysis using machine learning, but still face challenges with ambiguity. Future chatbots may be able to pass the Turing Test by more closely resembling human conversations. Oracle's Intelligent Bot platform includes components for natural language processing, customization of bot flows, and integration with backend systems.
The document discusses artificial intelligence and its applications. It defines AI as the science and engineering of making intelligent machines, especially computer programs, and using computers to understand human intelligence. It notes that AI is seen in recommender systems, virtual assistants, face and object recognition used by companies like YouTube, Facebook, Amazon, Google. The document also discusses machine learning, the Turing test, game playing, applications of AI in healthcare like genome editing and nanobots, using AI for social good like reducing pollution and crime, and potential problems like unemployment and threats to privacy.
Chat-bots y el futuro de las apps sin interfaz - ChatbotsLuis Díaz del Dedo
Charla sobre chat-bots que impartimos en Growth Hack Spain. En este documento podrás aprender: Breve historia de los chat-bots
¿¡Por qué ahora!?
Motivos por los que ES el momento de los chat-bots
Tipos de chat-bots
Tecnología
Aplicaciones principales
Conclusiones
Building bots to automate common developer tasks - Writing your first smart c...Sigmoid
Human Communication
Online Communication
Messaging today
Why Messaging Apps might take over native apps
Why the sudden Bot uprising?
What is a Bot?
What makes a great bot?
Design principles
Common pitfalls
Before starting to develop a Bot
Helpful tools
Simple architecture
Demo: Uber Bot
References
Ondrisek @ DevTernity "Insights into Chatbot Development - Implementing Cros...Barbara Ondrisek
This document discusses insights into developing cross-platform chatbots. It provides background on chatbots and natural language processing. It then examines popular messenger apps and their usage numbers. Examples are given of commercial and non-commercial chatbots implemented on platforms like Facebook Messenger and Skype. The document outlines chatbot architecture and processing pipelines. It compares features available across different platforms and highlights lessons learned in chatbot development.
My talk at the Stockholm Natural Language Processing Meetup. I explained how word2vec is implemented and how to use it in Python with gensim. When words are represented as points in space, the spatial distance between words describes a similarity between these words. In this talk, I explore how to use this in practice and how to visualize the results (using t-SNE)
The document provides tips for developing Korean chatbots, including discussing chatbot goals, architectures, data collection, natural language processing tools, and machine learning algorithms. It recommends focusing chatbots for business on a small number of important intents, using a modular architecture for easier debugging, and training natural language tools on domain-specific data collected from sources like web scraping.
Conversation diagram for Econsultancy's Facebook chatbotEconsultancy
This deck shows the conversation diagram for Econsultancy's Facebook Messenger chatbot. Created by Byte London, it details the bot's automated responses and conversation pathways.
Discover the tech & digital trends to watch in 2017 based on our experience: AI & Machine Learning, Deep Learning, Cognitive Computing, Conversational Interfaces, Bots, Mixed Reality, Gesture-based controls, Omnichannel, Mobile Commerce, Mobile-only Customer Experience, Deep Linking, Video 360°, Real-time Fact Checking, System Integration, Digital Transformation and Experience Business.
This document discusses recent advances in seq2seq learning. It begins with an overview of recurrent neural networks and LSTMs, which are used in seq2seq models. Seq2seq models are introduced as a way to map an input sequence to an output sequence without requiring explicit segmentation. The seq2seq idea involves using an encoder to represent the input sequence and a decoder to generate the output sequence. Attention mechanisms are discussed as a way to allow the decoder to focus on different parts of the input sequence. Applications mentioned include machine translation, image captioning, grammar parsing, and conversational bots.
Building Modern Applications Using APIs, Microservices and ChatbotsOracle Developers
The document discusses modern application development using APIs, microservices, and chatbots. It outlines how application development has changed from hardcoded monolithic applications to dynamic experiences composed of microservices and APIs. It then discusses key requirements for modern applications including polyglot development, microservices, DevOps tools, and support for APIs, chatbots and mobile. The document provides examples of building applications using these techniques for tasks like connecting fans to sports games.
word2vec, LDA, and introducing a new hybrid algorithm: lda2vec👋 Christopher Moody
This document summarizes the lda2vec model, which combines aspects of word2vec and LDA. Word2vec learns word embeddings based on local context, while LDA learns document-level topic mixtures. Lda2vec models words based on both their local context and global document topic mixtures to leverage both approaches. It represents documents as mixtures over sparse topic vectors similar to LDA to maintain interpretability. This allows it to predict words based on local context and global document content.
El documento describe el Síndrome de Lejeune o "cri du chat", un desorden cromosómico causado por una deleción en el cromosoma 5 que causa discapacidad intelectual, retraso en el desarrollo, características faciales distintivas y otros problemas de salud. Se requiere un tratamiento multidisciplinario y el apoyo de la familia para ayudar al individuo a alcanzar su máximo potencial. El caso clínico presentado ilustra los retos médicos y de cuidado que enfrenta un paciente de
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The results of the global Energy Architecture Performance Index (EAPI) 2017 highlight key trends in the energy transition moving towards more sustainable, affordable and secure energy systems around the world, as well as the challenges countries continue to face, individually and as cohorts. Looking back at five years of data from the EAPI, this report also distils insights from countries that have shown significant improvements in performance or remained consistently high performers
La Fiscalía General de la Nación es una entidad de la Rama Judicial que investiga y acusa a los presuntos responsables de cometer delitos. Está integrada por el Fiscal General y otros funcionarios como fiscales delegados. Sus principales funciones son investigar delitos de oficio o por denuncia, acusar a los infractores ante los juzgados, y diseñar la política criminal del Estado.
O documento descreve o processo de elaboração de um plano de gestão de cargos e salários, incluindo a descrição de cargos, pesquisa salarial, e desenho da política de remuneração. O processo envolve análise, coleta de informações, elaboração e avaliação das descrições de cargos, pesquisa salarial e desenho da política de remuneração. Fatores como experiência, esforço físico e mental, responsabilidades, erros e valores são utilizados para avaliar os cargos.
This document summarizes a presentation on using an LSTM neural network to predict bitcoin price movements based on sentiment analysis of twitter data. It describes collecting over 1 million tweets related to bitcoin, representing the words in the tweets as word vectors, training an LSTM model on the vectorized tweet data with sentiment labels, and evaluating whether the predicted sentiment correlates with bitcoin price changes. While the results did not find a relationship between sentiment and price according to this model, improvements are discussed such as using a training set more similar to the actual tweet data.
Natural Language Generation / Stanford cs224n 2019w lecture 15 Reviewchangedaeoh
This document discusses natural language generation (NLG) tasks and neural approaches. It begins with a recap of language models and decoding algorithms like beam search and sampling. It then covers NLG tasks like summarization, dialogue generation, and storytelling. For summarization, it discusses extractive vs. abstractive approaches and neural methods like pointer-generator networks. For dialogue, it discusses challenges like genericness, irrelevance and repetition that neural models face. It concludes with trends in NLG evaluation difficulties and the future of the field.
Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better!
In this lecture, we will get an introduction to Autoencoders and Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano. This will be a preview of the QuantUniversity Deep Learning Workshop that will be offered in 2017.
This document provides an overview of using deep learning algorithms like LSTM and sentiment analysis to predict bitcoin prices. It discusses neural networks and RNNs, why LSTMs are better than RNNs at capturing long-term dependencies. It describes implementing an LSTM model to predict prices from historical data and analyzing sentiment from Twitter tweets. Testing was done and results showed the model could predict prices. Future work includes applying it to other cryptocurrencies and improving performance.
Halvar Flake and Sebastian Porst present BinCrowd, a tool for analyzing disassembled binaries. It allows uploading analysis results to a central database for later retrieval and comparison to other binaries. This helps identify code reuse across different programs. The presentation covers techniques for function matching and scoring file similarity. It also discusses how BinCrowd can be accessed using IDA Pro and managing access levels for team collaboration.
This document provides an introduction to Java programming. It discusses what computer science and programming are, and introduces basic Java concepts like classes, methods, and print statements. It also covers data types, variables, operators, and control structures that allow programmers to write algorithms and programs. The document uses examples like simple print programs and a cookie baking algorithm to demonstrate core Java programming concepts.
This document discusses practical aspects of natural language processing (NLP) work. It contrasts research work, which involves setting goals, devising algorithms, training models, and testing accuracy, with development work, which focuses on implementing algorithms as scalable APIs. The document emphasizes that obtaining data is crucial for NLP and describes sources for structured, semi-structured, and unstructured data. It recommends Lisp as a language that supports the interactivity, flexibility, and tree processing needed for NLP research and development work.
MDEC Data Matters Series: machine learning and Deep Learning, A PrimerPoo Kuan Hoong
The document provides an overview of machine learning and deep learning. It discusses the history and development of neural networks, including deep belief networks, convolutional neural networks, and recurrent neural networks. Applications of deep learning in areas like computer vision, natural language processing, and robotics are also covered. Finally, popular platforms, frameworks and libraries for developing deep learning models are presented, along with examples of pre-trained models that are available.
LLMs for the “GPU-Poor” - Franck Nijimbere.pdfGDG Bujumbura
Struggling with limited GPU resources but want to leverage large language models (LLMs)? This session provides a deep dive into cutting-edge LLM compression methods like quantization, pruning, and knowledge distillation. Learn how to efficiently run LLMs without sacrificing performance. Ideal for data scientists, machine learning engineers, and AI enthusiasts keen on cost-effective solutions. Includes a 5-minute Q&A.
This document discusses memory consistency models for parallel programs. It covers:
- Shared memory is the most common approach to parallel programming. The memory model defines what values a read can return.
- The sequential consistency (SC) model is the most intuitive but not practical due to performance issues. The data-race-free model provides SC for programs without data races.
- Specifying semantics for programs with data races is challenging. Both hardware and software models have issues that cause mismatches.
- Current approaches are fundamentally broken. Future work needs higher-level models that enforce discipline and hardware designed together with language models. Deterministic parallel programming should be the default approach.
EchoBay is a framework that uses Bayesian optimization to efficiently optimize hyperparameters for echo state networks (ESN), a type of recurrent neural network. It reduces the expertise required for users and provides automatic hyperparameter selection. Bayesian optimization requires fewer data samples than grid search to find optimal hyperparameters. EchoBay allows non-experts to rapidly train ESN models through an easy-to-use interface without requiring code writing.
This document summarizes the three tracks of the DSTC6 dialogue system technology challenges. Track 1 focuses on end-to-end goal oriented dialog learning with a restaurant reservation domain. Track 2 focuses on end-to-end conversation modeling to generate responses using dialogue history and external knowledge. Track 3 aims to detect breakdowns in dialogues using classification metrics.
This document discusses modeling network traffic behaviors to detect botnets. It proposes modeling individual network connections as state sequences based on flow size, duration, and periodicity. Connections are grouped by source/destination IP, port, and protocol. Labeled network data is used to train Markov chain models of normal and botnet connection behaviors. These models are then tested on unlabeled data to detect similar behaviors and identify botnet connections with over 70% accuracy on average. Compared methods achieve lower accuracy rates, showing this behavioral modeling approach is effective for botnet detection.
Writing code that works and writing code that other people can read and understand are two different skills. And writing code that other people can read and understand became more and more essential skills as the project grows larger, and more people start working on it.
But because it is a skill, you need to train it consciously. It's a lot like writing essays and books. Everybody can write letters and words; many can also connect them in grammatically correct sentences. But not everybody is a J. R. R. R. Tolkien and have their books read by everyone.
An essential part of learning this skill is reading and analyzing other people's code on the one hand. And making other people read your code and give you feedback about it.
The speaker will talk about different methods of how to make programmers better writers and how to work out the skill of writing code that other people will want to read.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
1. Chatbot
Sequence to Sequence Learning
29 Mar 2017
Presented By:
Jin Zhang
Yang Zhou
Fred Qin
Liam Bui
Overview
Network
Architecture
Loss Function
Improvement
Techniques
3. Overview
Network
Architecture
Loss Function
Improvement
Techniques
LSTM for Language Model
• Language Model
Predict next word given the previous words
• RNN
Unable to learn long term dependency, not suitable for language model
• LSTM
3 sigmoid gates to control info flow
Understanding LSTM Networks: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
4. Overview
Network
Architecture
Loss Function
Improvement
Techniques
• First step: which previous information to throw
away from the cell state
LSTM for Language Model
• Second step: what new information to be
stored in the cell state
- A sigmoid layer decides which values to
update
- A tanh layer creates new candidate values
C~t that could be added to the state
- Combine these two to create an update to
the state
• Third step: filter Ct and output only what we
want to output
Understanding LSTM Networks: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
5. Seq2Seq model comprises of two language models:
• Encoder: a language model to encode input sequence into a fixed length vector (thought vector)
• Decoder: another language model to look at both thought vector and previous output to generate next words
Overview
Network
Architecture
Loss Function
Improvement
Techniques
Sequence To Sequence Model
Neural Machine Translation by Jointly Learning to Align and Translate: https://arxiv.org/abs/1409.0473
8. Overview
Network
Architecture
Loss Function
Improvement
Techniques
Generating a word is a multi-class classification task over all possible words, i.e. vocabulary.
W* = argmaxW P(W|Previous words)
Example :
I always order pizza with cheese and ……
mushrooms 0.15
pepperoni 0.12
anchovies 0.01
….
rice 0.0001
and 1e-100
Loss Function
9. Cross Entropy Loss:
Cross-Entropy:
Cross-Entropy for a sentence w1, w2, …, wn:
Overview
Network
Architecture
Loss Function
Improvement
Techniques
Evaluating Language Model: https://courses.engr.illinois.edu/cs498jh/Slides/Lecture04.pdf
Perplexity:
In practice, a variant called perplexity is usually used as metric to evaluate language models.
10. • Cross entropy can be seen as a measure of uncertainty
• Perplexity can be seen as “number of choices”
Overview
Network
Architecture
Loss Function
Improvement
Techniques
Cross entropy loss vs Perplexity:
• Entropy: ~2.58
• Perplexity: 6 choices
• Which statement do you prefer?
- The die has 6 faces
- The die has 2.58 entropy
• We can see perplexity as the average choices each time. The higher it is, the more “choices” of words
you have, then the more uncertain the language model is.
• Example: 6 faced balanced die. Each face is numbered from 1 to 6 so we have
11. Overview
Network
Architecture
Loss Function
Improvement
Techniques
Problem:
- The last state of the encoder contains mostly
information from the last elements of the
encoder sequence
- Inverse Input Sequence helps in some cases
How are you ?
I am fine .
Attention Mechanism:
- Allow each stage in decoder to look at any
encoder stages
- Decoder understand the input sentence more
and look at suitable positions to generate words
Neural Machine Translation by Jointly Learning to Align and Translate: https://arxiv.org/abs/1409.0473
12. Overview
Network
Architecture
Loss Function
Improvement
Techniques
Problem:
- The last state of the encoder contains mostly
information from the last elements of the
encoder sequence
- Inverse Input Sequence helps in some cases
Attention Mechanism:
- Allow each stage in decoder to look at any
encoder stages
- Decoder understand the input sentence more
and look at suitable positions to generate words
Neural Machine Translation by Jointly Learning to Align and Translate: https://arxiv.org/abs/1409.0473
Seq2Seq Seq2Seq with
attention
Sentence Length - 30 13.93 21.50
Sentence Length - 50 17.82 28.45
BLEU score on English-French Translation corpus
13. Overview
Network
Architecture
Loss Function
Improvement
Techniques
Problem:
- Maximizing conditional probabilities at each stage
might not lead to maximum full-joint probability.
- Storing all possible generated sentences are not
feasible due to resource limitation.
Possible output 2: Never been better
Possible output 1: I am fine
Beam Search:
- At each stage in decoder, store best M possible
outputs
Sequence to Sequence Learning: https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf
Conditional Probability: 0.6 0.4 1
Conditional Probability: 0.4 0.9 1
Full-joint
Probability:
0.24
0.36
Possible Output 1:
Possible Output 2:
Possible Output M:
How are you ?
I am fine .
…
14. Overview
Network
Architecture
Loss Function
Improvement
Techniques
Problem:
- Maximizing conditional probabilities at each stage
might not lead to maximum full-joint probability.
- Storing all possible generated sentences are not
feasible due to memory limitation.
Beam Search:
- At each stage in decoder, store best M possible
outputs
Sequence to Sequence Learning: https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf
Seq2Seq with
beam-size = 1
Seq2Seq with
beam size = 12
28.45 30.59
BLEU score on English-French Translation corpus.
Max sentence length 50
18. Overview
Network
Architecture
Loss Function
Improvement
Techniques
1. Reinforcement Learning:
Longer sentence is usually more interesting. So, we can use sentence length as rewards to further train the model:
• Action: Word choice
• State: Current generated sentence
• Reward: Sentence Length
2. Adversarial Training:
Make generated sentences look real using Adversarial training:
• Generative Model: generate sentences based on inputs
• Discriminant Model: tries to tell if a sentence is true response or generated response
• Objective: train generative model to “fool“ discriminant model
Adversarial Learning for Neural Dialogue Generation: https://arxiv.org/abs/1701.06547
Editor's Notes
First of all, let’s see a demo. This is a customer service chatbot demo. We can see that you can let it find an order as easy as chatting with a person. That’s why chatbot is a very hot topic. Many companies are working on various kinds of chatbots, including travel search engine, personal health companion and so on.
There are three ways to compare chatbots.
Retrieval-based models don’t generate any new text, they just pick a response from a predefined responses repository based on the input and context. In such case, retrieval-based methods don’t make grammatical mistakes. However, they may be unable to handle unseen cases for which no appropriate predefined response exists. For the same reasons, these models can’t refer back to contextual entity information like names mentioned earlier in the conversation.
However, Generative models don’t rely on pre-defined response. They generate new responses from scratch. Generative models are “smarter”. They can refer back to entities in the input and give the impression that you’re talking to a human. However, these models are hard to train, are quite likely to make grammatical mistakes (especially on longer sentences), and typically require huge amounts of training data. Generative models are “smarter”. They can refer back to entities in the input and give the impression that you’re talking to a human.
Chatbots can be built to support short-text conversations, such as FAQ chatbot, or long conversations, such as customer support chatbot.
Chatbots can be set to closed domain or open domain. The demo of this customer service chatbot is an example of the closed domain, in which the questions and answers are limited to specific area. In an open domain (harder) setting the user can take the conversation anywhere, such as siri.
Retrieval-based models (easier) use a repository of predefined responses and some kind of heuristic to pick an appropriate response based on the input and context. The heuristic could be as simple as a rule-based expression match, or as complex as an ensemble of Machine Learning classifiers. These systems don’t generate any new text, they just pick a response from a fixed set.
Due to the repository of handcrafted responses, retrieval-based methods don’t make grammatical mistakes. However, they may be unable to handle unseen cases for which no appropriate predefined response exists. For the same reasons, these models can’t refer back to contextual entity information like names mentioned earlier in the conversation. Generative models are “smarter”. They can refer back to entities in the input and give the impression that you’re talking to a human. However, these models are hard to train, are quite likely to make grammatical mistakes (especially on longer sentences), and typically require huge amounts of training data.
Short-Text Conversations (easier) where the goal is to create a single response to a single input. For example, you may receive a specific question from a user and reply with an appropriate answer. Then there are long conversations (harder) where you go through multiple turns and need to keep track of what has been said. Customer support conversations are typically long conversational threads with multiple questions.
In a closed domain (easier) setting the space of possible inputs and outputs is somewhat limited because the system is trying to achieve a very specific goal. Technical Customer Support or Shopping Assistants are examples of closed domain problems.
In an open domain (harder) setting the user can take the conversation anywhere. There isn’t necessarily have a well-defined goal or intention. The infinite number of topics and the fact that a certain amount of world knowledge is required to create reasonable responses makes this a hard problem.
The foundation of building a chatbot is language modelling. Generally speaking, a language model takes in a sequence of inputs, looks at each element of the sequence and tries to predict the next element of the sequence.
In theory, RNNs are absolutely capable of handling such “long-term dependencies.” A human could carefully pick parameters for them to solve toy problems of this form. Sadly, in practice, RNNs don’t seem to be able to learn them.
LSTMs are explicitly designed to avoid the long-term dependency problem.
The key to the LSTM is the cell state, easy for information to just flow along it unchanged.
The sigmoid layer outputs numbers between zero and one, describing how much of each component should be let through. A value of zero means “let nothing through,” while a value of one means “let everything through!”
An LSTM has three of these gates, to protect and control the cell state.
The first step in our LSTM is to decide what information we’re going to throw away from the cell state. This decision is made by a sigmoid layer called the “forget gate layer.” It looks at ht-1 and xt and outputs a number between 0 and 1 for each number in the cell state Ct-1. A
1 represents “completely keep this” while a 0 represents “completely get rid of this.”
Let’s go back to our example of a language model trying to predict the next word based on all the previous ones. In such a problem, the cell state might include the gender of the present subject, so that the correct pronouns can be used. When we see a new subject, we want to forget the gender of the old subject.
The next step is to decide what new information we’re going to store in the cell state. This has two parts. First, a sigmoid layer called the “input gate layer” decides which values we’ll update. Next, a tanh layer creates a vector of new candidate values, C~t, that could be added to the state. In the next step, we’ll combine these two to create an update to the state.
In the example of our language model, we’d want to add the gender of the new subject to the cell state, to replace the old one we’re forgetting.
It’s now time to update the old cell state, C~t-1into the new cell state C~t. The previous steps already decided what to do, we just need to actually do it.
We multiply the old state by ft, forgetting the things we decided to forget earlier. Then we add C~t*it .This is the new candidate values, scaled by how much we decided to update each state value.
In the case of the language model, this is where we’d actually drop the information about the old subject’s gender and add the new information, as we decided in the previous steps.
Finally, we need to decide what we’re going to output. This output will be based on our cell state, but will be a filtered version. First, we run a sigmoid layer which decides what parts of the cell state we’re going to output. Then, we put the cell state through tanh (to push the values to be between −1 and 1) and multiply it by the output of the sigmoid gate, so that we only output the parts we decided to.
For the language model example, since it just saw a subject, it might want to output information relevant to a verb, in case that’s what is coming next. For example, it might output whether the subject is singular or plural, so that we know what form a verb should be conjugated into if that’s what follows next.
RNNs can be used as language models for predicting future elements of a sequence given prior elements of the sequence. However, we are still missing the components necessary for building translation models since we can only operate on a single sequence, while translation operates on two sequences – the input sequence and the translated sequence.
Sequence to sequence models build on top of language models by adding an encoder step and a decoder step. In the encoder step, a model converts an input sequence into a thought vector. In the decoder step, a language model is trained on both the output sequence as well as the thought vector from the encoder. Since the decoder model sees an encoded representation of the input sequence as well as the output sequence, it can make more intelligent predictions about future words based on the current word.
For example, in a standard language model, we might see the word “crane” and not be sure if the next word should be about the bird or heavy machinery. However, if we also pass an encoder context, the decoder might realize that the input sequence was about construction, not flying animals. Given the context, the decoder can choose the appropriate next word and provide more accurate reply.
Now that we understand the basics of sequence to sequence modeling, we can consider how to build one. We will use LSTM as encoder and decoder.
The encoder takes a sequence(sentence) as input and processes one symbol(word) at each time step. Its objective is to convert a sequence of symbols into a fixed size feature vector that encodes only the important information in the sequence while losing the unnecessary information.
Each hidden state influences the next hidden state and the final hidden state can be seen as the summary of the sequence. This state is called the context or thought vector, as it represents the intention of the sequence. From the context, the decoder generates another sequence, one symbol(word) at a time. Here, at each time step, the decoder is influenced by the context and the previously generated symbols.
We can train the model using a gradient-based algorithm, update parameters of encoder and decoder, jointly maximize the log probability of the output sequence conditioned on the input sequence. Once the model is trained, we can make predictions
The context can be provided as the initial state of the decoder RNN or it can be connected to the hidden units at each time step. Now our objective is to jointly maximize the log probability of the output sequence conditioned on the input sequence.
whichever a model gives us the highest prop for all the words should be our model.
Evaluate per word perplexity. For the probability, we definitely think of cross entropy.
https://courses.engr.illinois.edu/cs498jh/Slides/Lecture04.pdf
by applying the chain rule, we can get perplexity per word.
Compressing an entire input sequence into a single fixed vector is challenging. The last state of the encoder contains mostly information from the last elements of the encoder sequence
This mechanism will hold onto all states from the encoder and give the decoder a weighted average of the encoder states for each element of the decoder sequence
During the decoding phase, we take the state of the decoder network, combine it with the encoder states, and pass this combination to a feedforward network. The feedforward network returns weights for each encoder state. We multiply the encoder inputs by these weights and then compute a weighted average of the encoder states.
BLEU (bilingual evaluation understudy): measures the correspondence between a machine's output and that of a human
BLEU = sum: max(word count in generated sentence, word count in referenced sentence)/total generated sentence length for each word
Maximizing conditional probabilities at each stage might not lead to maximum full-joint probability.
We could store all possible generated sentences so that we always find the maximum full-joint probability, but it would not be feasible.
A practical solution would be something in between.