A neural conversational_model
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This paper proposes a neural conversational model that uses a sequence-to-sequence framework to generate answers to questions. It can be trained end-to-end without many hand-crafted rules and can generate simple conversations given large training data. The model adopts the sequence-to-sequence framework and is trained using backpropagation to maximize cross entropy. During inference it uses greedy decoding but beam search could also be used. The model was verified by having humans rate it against CleverBot on 200 questions and it can generate basic conversations and answer questions without rules.
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Machine Reading Comprehension (MRC), particularly extractive close-domain question-answering, is a prominent field in Natural Language Processing (NLP). Given a question and a passage or set of passages, a machine must be able to extract the appropriate answer from the passage(s). However, the majority of these existing questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Runtime of base models on the entire datasetis approximately one day while the runtime for all models on a third of the dataset is a little over two days. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset compared to the single-span source datasets. While the models tested on the source datasets were slightly fine-tuned in order to return multiple answers, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods
French machine reading for question answering
This paper proposes to unlock the main barrier to machine reading and comprehension French natural language texts. This open the way to machine to find to a question a precise answer buried in the mass of unstructured French texts. Or to create a universal French chatbot. Deep learning has produced extremely promising results for various tasks in natural language understanding particularly topic classification, sentiment analysis, question answering, and language translation. But to be effective Deep Learning methods need very large training da-tasets. Until now these technics cannot be actually used for French texts Question Answering (Q&A) applications since there was not a large Q&A training dataset. We produced a large (100 000+) French training Dataset for Q&A by translating and adapting the English SQuAD v1.1 Dataset, a GloVe French word and character embed-ding vectors from Wikipedia French Dump. We trained and evaluated of three different Q&A neural network ar-chitectures in French and carried out a French Q&A models with F1 score around 70%.
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Writing a scientific manuscript
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IRJET- Semantic Question Matching
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Natural Language Processing
This document discusses recent advances in machine learning for natural language processing using recurrent neural networks and word embeddings. It summarizes that neural networks trained on huge datasets can now understand human language better than ever before. Word embeddings represent words as dense vectors that encode semantic meaning, allowing machines to understand relationships between words. The document then discusses using recurrent neural networks and LSTM models to generate text, and provides an example output from a character-level text generation model trained on Alice in Wonderland.
Convolutional recurrent neural network with template based representation for...
Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.
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In this talk we cover
1. Why NLP and DL
2. Practical Challenges
3. Some Popular Deep Learning models for NLP
Today you can take any webpage in any language and translate it automatically into language you know! You can also cut paste an article or other document into NLP systems and immediately get list of companies and people it talks about, topics that are relevant and the sentiment of the document. When you talk to Google or Amazon assistant, you are using NLP systems. NLP is not perfect but given the advances in last two years and continuing, it is a growing field. Let’s see how it actually works, specifically using Deep learning
About Shishir
Shishir is a Senior Data Scientist at Thomson Reuters working on Deep Learning and NLP to solve real customer pain, even ones they have become used to.
QUESTION ANSWERING MODULE LEVERAGING HETEROGENEOUS DATASETS
Question Answering has been a well-researched NLP area over recent years. It has become necessary for
users to be able to query through the variety of information available - be it structured or unstructured. In
this paper, we propose a Question Answering module which a) can consume a variety of data formats - a
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modules. Our solution is capable of handling a plethora of data sources such as text, images, tables,
community forums, and flow charts. Our studies performed on a variety of business-specific datasets
represent the necessity of custom pipelines like the proposed one to solve several real-world document
question-answering
QUESTION ANSWERING MODULE LEVERAGING HETEROGENEOUS DATASETS
The document describes an intelligent question answering system that can leverage heterogeneous datasets including product manuals, technical forums, and discussion threads. The system includes four main modules: 1) A document parser that can parse different data types including text, images, tables, and forums using deep learning models. 2) A document indexer that indexes documents for retrieval. 3) A document retriever that handles query processing and identifies relevant text segments. 4) A document reader that provides answers by analyzing relevant text segments. The system aims to reduce the time needed to find answers across different data sources by automatically identifying the most relevant information for a given question.
Open domain Question Answering System - Research project in NLP
Using a computer to answer questions has been a human dream since the beginning of the digital era. A first step towards the achievement of such an ambitious goal is to deal with natural language to enable the computer to understand what its user asks. The discipline that studies the connection between natural language and the representation of its meaning via computational models is computational linguistics. According to such discipline, Question Answering can be defined as the task that, given a question formulated in natural language , aims at finding one or more concise answers. And the Improvements in Technology and the Explosive demand for better information access has reignited the interest in Q & A systems , The wealth of the information on the web makes it an Interactive resource for seeking quick Answers to factual Questions such as “Who is the first American to land in space ?”, or “what is the second Tallest Mountain in the world ?”, yet Today’s Most advanced web Search systems(Bing , Google , yahoo) make it Surprisingly Tedious to locate the Answers , Q& A System Aims to develop techniques that go beyond Retrieval of Relevant documents in order to return the exact answers using Natural language factoid question
Prior On Model Space
What makes a model simple? Do we know what is likely before we see data? Can we use this to make better models. Existing and new approaches for bringing in more knowledge to solve machine learning problems.
Deep Learning for NLP (without Magic) - Richard Socher and Christopher Manning
The document discusses deep learning for natural language processing. It provides 5 reasons why deep learning is well-suited for NLP tasks: 1) it can automatically learn representations from data rather than relying on human-designed features, 2) it uses distributed representations that address issues with symbolic representations, 3) it can perform unsupervised feature and weight learning on unlabeled data, 4) it learns multiple levels of representation that are useful for multiple tasks, and 5) recent advances in methods like unsupervised pre-training have made deep learning models more effective for NLP. The document outlines some successful applications of deep learning to tasks like language modeling and speech recognition.
Paper id 28201441
The document discusses using support vector machines (SVM) and various lexical, semantic, and syntactic features for question classification. It aims to develop a state-of-the-art machine learning based question classifier. Various features are discussed, including lexical features like n-grams and stemming, syntactic features like question headwords, and semantic features derived from named entity recognition, WordNet senses, and semantic word lists. SVM is used as the classifier to take advantage of its good performance for text classification tasks. The results show that combining these feature types can achieve accurate question classification.
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A neural conversational_model
- 2. What is this paper about? It proposed a neural conversational model which generates an answer to a question using sequence to sequence framework.
- 3. What’s better than previous paper? This model can be trained end-to-end and thus requires much fewer hand-craft rules. This model can generate simple conversations given a large conversational training dataset. This model has its simplicity and generality and thus can be applied to machine translation, question/answering, and conversations without major changes in the architecture.
- 4. What’re important parts of technic and method? This model adopted sequence to sequence framework and be done by backpropagation in learning to maximize the cross entropy of the correct sequence. During inference, this model used a greedy inference approach. A less greedy approach would be to use beam search.
- 5. How did they verify it? They Asked four different humans to rate this model versus CleverBot with picked 200 questions from http://ai.stanford.edu/~quocle/QAresults.pdf.
- 6. Is there a discussion? It can generate simple and basic conversations, extract knowledge and answer to many types of questions without any rules.
- 7. Is there a paper to read next? Neural machine translation RNNs LSTM Sequence to sequence framework