This document summarizes a workshop on the Language Grid, which is a service-oriented infrastructure for multilingual societies. It discusses how the Language Grid provides various language services, such as machine translation, dictionaries, and parallel texts. It also describes how these atomic services can be composed to create new multilingual applications and services. Finally, it outlines several research projects using the Language Grid, including analyzing machine translation-mediated communication, developing multilingual localization systems, and extending the Language Grid's capabilities.
Improvement in Quality of Speech associated with Braille codes - A Reviewinscit2006
J. Anurag, P. Nupur and Agrawal, S.S.
School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India
Centre for Development of Advanced Computing, Noida, India
Improvement in Quality of Speech associated with Braille codes - A Reviewinscit2006
J. Anurag, P. Nupur and Agrawal, S.S.
School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India
Centre for Development of Advanced Computing, Noida, India
A New Approach: Automatically Identify Naming Word from Bengali Sentence for ...Syeful Islam
More than hundreds of millions of people of almost all levels of education and attitudes from different country communicate with each other for different purposes using various languages. Machine translation is highly demanding due to increasing the usage of web based Communication. One of the major problem of Bengali translation is identified a naming word from a sentence, which is relatively simple in English language, because such entities start with a capital letter. In Bangla we do not have concept of small or capital letters and there is huge no. of different naming entity available in Bangla. Thus we find difficulties in understanding whether a word is a naming word or not. Here we have introduced a new approach to identify naming word from a Bengali sentence for machine translation system without storing huge no. of naming entity in word dictionary. The goal is to make possible Bangla sentence conversion with minimal storing word in dictionary.
Human Evaluation: Why do we need it? - Dr. Sheila CastilhoSebastian Ruder
Talk at the 8th NLP Dublin meetup (https://www.meetup.com/NLP-Dublin/events/241198412/) by Dr. Sheila Castilho, postdoc at ADAPT Centre, Dublin City University.
Design Analysis Rules to Identify Proper Noun from Bengali Sentence for Univ...Syeful Islam
Abstract—Now-a-days hundreds of millions of people of
almost all levels of education and attitudes from different
country communicate with each other for different
purposes and perform their jobs on internet or other
communication medium using various languages. Not all
people know all language; therefore it is very difficult to
communicate or works on various languages. In this
situation the computer scientist introduce various inter
language translation program (Machine translation). UNL
is such kind of inter language translation program. One of
the major problem of UNL is identified a name from a
sentence, which is relatively simple in English language,
because such entities start with a capital letter. In Bangla
we do not have concept of small or capital letters. Thus
we find difficulties in understanding whether a word is a
proper noun or not. Here we have proposed analysis rules
to identify proper noun from a sentence and established
post converter which translate the name entity from
Bangla to UNL. The goal is to make possible Bangla
sentence conversion to UNL and vice versa. UNL system
prove that the theoretical analysis of our proposed system
able to identify proper noun from Bangla sentence and
produce relative Universal word for UNL.
Detection of Verbal Multi-Word Expressions via Conditional Random Fields with...Lifeng (Aaron) Han
ADAPT Centre & Detection of Verbal Multi-Word Expressions via Conditional Random Fields with Syntactic Dependency Features and Semantic Re-Ranking @ DLSS2017 Bilbao.
A prior case study of natural language processing on different domain IJECEIAES
In the present state of digital world, computer machine do not understand the human’s ordinary language. This is the great barrier between humans and digital systems. Hence, researchers found an advanced technology that provides information to the users from the digital machine. However, natural language processing (i.e. NLP) is a branch of AI that has significant implication on the ways that computer machine and humans can interact. NLP has become an essential technology in bridging the communication gap between humans and digital data. Thus, this study provides the necessity of the NLP in the current computing world along with different approaches and their applications. It also, highlights the key challenges in the development of new NLP model.
How can text-mining leverage developments in Deep Learning? Presentation at ...jcscholtes
How can text-mining leverage developments in Deep Learning?
Text-mining focusses primary on extracting complex patterns from unstructured electronic data sets and applying machine learning for document classification. During the last decade, a generation of efficient and successful algorithms has been developed using bag-of-words models to represent document content and statistical and geometrical machine learning algorithms such as Conditional Random Fields and Support Vector Machines. These algorithms require relatively little training data and are fast on modern hardware. However, performance seems to be stuck around 90% F1 values.
In computer vision, deep learning has shown great success where the 90% barrier has been broken in many application. In addition, deep learning also shows new successes for transfer learning and self-learning such as reinforcement leaning. Dedicated hardware helped us to overcome computational challenges and methods such as training data augmentation solved the need for unrealistically large data sets.
So, it would make sense to apply deep learning also on textual data as well. But how do we represent textual data: there are many different methods for word embeddings and as many deep learning architectures. Training data augmentation, transfer learning and reinforcement leaning are not fully defined for textual data.
A New Approach: Automatically Identify Proper Noun from Bengali Sentence for ...Syeful Islam
More than hundreds of millions of people of almost all levels of education and attitudes from different country communicate with
each other for different using various languages. Machine translation is highly demanding due to increasing the usage of web
based Communication. One of the major problem of Bengali translation is identified a naming word from a sentence, which is
relatively simple in English language, because such entities start with a capital letter. In Bangla we do not have concept of small
or capital letters and there is huge no. of different naming entity available in Bangla. Thus we find difficulties in understanding
whether a word is a proper noun or not. Here we have introduce a new approach to identify proper noun from a Bengali sentence
for UNL without storing huge no. of naming entity in word dictionary. The goal is to make possible Bangla sentence conversion
to UNL and vice versa with minimal storing word in dictionary.
The NLP muppets revolution! @ Data Science London 2019
video: https://skillsmatter.com/skillscasts/13940-a-deep-dive-into-contextual-word-embeddings-and-understanding-what-nlp-models-learn
event: https://www.meetup.com/Data-Science-London/events/261483332/
Nonparametric Bayesian Word Discovery for Symbol Emergence in RoboticsTadahiro Taniguchi
This is a material for invited talk in the workshop on Machine Learning Methods for High-
Level Cognitive Capabilities in Robotics 2016 (ML-HLCR2016) held in IROS2016, Korea.
Machine translation from English to HindiRajat Jain
Machine translation a part of natural language processing.The algorithm suggested is word based algorithm.We have done Translation from English to Hindi
submitted by
Garvita Sharma,10103467,B3
Rajat Jain,10103571,B6
A New Approach: Automatically Identify Naming Word from Bengali Sentence for ...Syeful Islam
More than hundreds of millions of people of almost all levels of education and attitudes from different country communicate with each other for different purposes using various languages. Machine translation is highly demanding due to increasing the usage of web based Communication. One of the major problem of Bengali translation is identified a naming word from a sentence, which is relatively simple in English language, because such entities start with a capital letter. In Bangla we do not have concept of small or capital letters and there is huge no. of different naming entity available in Bangla. Thus we find difficulties in understanding whether a word is a naming word or not. Here we have introduced a new approach to identify naming word from a Bengali sentence for machine translation system without storing huge no. of naming entity in word dictionary. The goal is to make possible Bangla sentence conversion with minimal storing word in dictionary.
Human Evaluation: Why do we need it? - Dr. Sheila CastilhoSebastian Ruder
Talk at the 8th NLP Dublin meetup (https://www.meetup.com/NLP-Dublin/events/241198412/) by Dr. Sheila Castilho, postdoc at ADAPT Centre, Dublin City University.
Design Analysis Rules to Identify Proper Noun from Bengali Sentence for Univ...Syeful Islam
Abstract—Now-a-days hundreds of millions of people of
almost all levels of education and attitudes from different
country communicate with each other for different
purposes and perform their jobs on internet or other
communication medium using various languages. Not all
people know all language; therefore it is very difficult to
communicate or works on various languages. In this
situation the computer scientist introduce various inter
language translation program (Machine translation). UNL
is such kind of inter language translation program. One of
the major problem of UNL is identified a name from a
sentence, which is relatively simple in English language,
because such entities start with a capital letter. In Bangla
we do not have concept of small or capital letters. Thus
we find difficulties in understanding whether a word is a
proper noun or not. Here we have proposed analysis rules
to identify proper noun from a sentence and established
post converter which translate the name entity from
Bangla to UNL. The goal is to make possible Bangla
sentence conversion to UNL and vice versa. UNL system
prove that the theoretical analysis of our proposed system
able to identify proper noun from Bangla sentence and
produce relative Universal word for UNL.
Detection of Verbal Multi-Word Expressions via Conditional Random Fields with...Lifeng (Aaron) Han
ADAPT Centre & Detection of Verbal Multi-Word Expressions via Conditional Random Fields with Syntactic Dependency Features and Semantic Re-Ranking @ DLSS2017 Bilbao.
A prior case study of natural language processing on different domain IJECEIAES
In the present state of digital world, computer machine do not understand the human’s ordinary language. This is the great barrier between humans and digital systems. Hence, researchers found an advanced technology that provides information to the users from the digital machine. However, natural language processing (i.e. NLP) is a branch of AI that has significant implication on the ways that computer machine and humans can interact. NLP has become an essential technology in bridging the communication gap between humans and digital data. Thus, this study provides the necessity of the NLP in the current computing world along with different approaches and their applications. It also, highlights the key challenges in the development of new NLP model.
How can text-mining leverage developments in Deep Learning? Presentation at ...jcscholtes
How can text-mining leverage developments in Deep Learning?
Text-mining focusses primary on extracting complex patterns from unstructured electronic data sets and applying machine learning for document classification. During the last decade, a generation of efficient and successful algorithms has been developed using bag-of-words models to represent document content and statistical and geometrical machine learning algorithms such as Conditional Random Fields and Support Vector Machines. These algorithms require relatively little training data and are fast on modern hardware. However, performance seems to be stuck around 90% F1 values.
In computer vision, deep learning has shown great success where the 90% barrier has been broken in many application. In addition, deep learning also shows new successes for transfer learning and self-learning such as reinforcement leaning. Dedicated hardware helped us to overcome computational challenges and methods such as training data augmentation solved the need for unrealistically large data sets.
So, it would make sense to apply deep learning also on textual data as well. But how do we represent textual data: there are many different methods for word embeddings and as many deep learning architectures. Training data augmentation, transfer learning and reinforcement leaning are not fully defined for textual data.
A New Approach: Automatically Identify Proper Noun from Bengali Sentence for ...Syeful Islam
More than hundreds of millions of people of almost all levels of education and attitudes from different country communicate with
each other for different using various languages. Machine translation is highly demanding due to increasing the usage of web
based Communication. One of the major problem of Bengali translation is identified a naming word from a sentence, which is
relatively simple in English language, because such entities start with a capital letter. In Bangla we do not have concept of small
or capital letters and there is huge no. of different naming entity available in Bangla. Thus we find difficulties in understanding
whether a word is a proper noun or not. Here we have introduce a new approach to identify proper noun from a Bengali sentence
for UNL without storing huge no. of naming entity in word dictionary. The goal is to make possible Bangla sentence conversion
to UNL and vice versa with minimal storing word in dictionary.
The NLP muppets revolution! @ Data Science London 2019
video: https://skillsmatter.com/skillscasts/13940-a-deep-dive-into-contextual-word-embeddings-and-understanding-what-nlp-models-learn
event: https://www.meetup.com/Data-Science-London/events/261483332/
Nonparametric Bayesian Word Discovery for Symbol Emergence in RoboticsTadahiro Taniguchi
This is a material for invited talk in the workshop on Machine Learning Methods for High-
Level Cognitive Capabilities in Robotics 2016 (ML-HLCR2016) held in IROS2016, Korea.
Machine translation from English to HindiRajat Jain
Machine translation a part of natural language processing.The algorithm suggested is word based algorithm.We have done Translation from English to Hindi
submitted by
Garvita Sharma,10103467,B3
Rajat Jain,10103571,B6
Past, Present, and Future: Machine Translation & Natural Language Processing ...John Tinsley
This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016.
In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.
This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016.
In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.
Conversational AI Agents have become mainstream today due to significant advancements in the methods required to build accurate models, such as machine learning and deep learning, and, secondly, because they are seen as a natural fit in a wide range of domains, such as healthcare, e-commerce, customer service, tourism, and education, that rely heavily on natural language conversations in day-to-day operations. This rapid increase in demand has been matched by an equally rapid rate of research and development, with new products being introduced on a daily basis.
Learn More:https://bit.ly/3tBkT81
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Wreck a nice beach: adventures in speech recognitionStephen Marquard
Introduction to speech recognition and a description of a project to integrate CMU Sphinx into the Opencast Matterhorn lecture capture system, focusing on language model adaptation using Wikipedia as a corpus.
کچھ عرصہ قبل جامعہ گجرات کے شعبہ علوم ترجمعہ میں ایک پرزنٹیشن دینے کا موقع ملا۔ محترم محمد کامران لیکچرار ڈیپارٹمنٹ ہذا کی خواہش پر سلائڈز شئر کر دی گئی ہیں سلائڈز مندرجہ ذیل لنک سے حاصل کی جا سکتی ہیں۔
پرزنٹیشن کی ویڈیو انشاءاللہ جلد اپلوڈ کر دی جائے گی۔
SpokenMedia Project: Media-Linked Transcripts and Rich Media Notebooks for Le...Brandon Muramatsu
The SpokenMedia project’s goal is to increase the effectiveness of web-based lecture media by improving the search and discoverability of specific, relevant media segments. SpokenMedia creates media-linked transcripts that will enable users to find contextually relevant video segments to improve their teaching and learning. The SpokenMedia project envisions a number of tools and services layered on top of, and supporting, these media-linked transcripts to enable users to interact with the media in more educationally relevant ways. Presented by Brandon Muramatsu to the IEEE-Computer Society Bangalore Section on August 6, 2009. (Unfortunately I didn't record the audio from this presentation :(, I thought it went really well and would have made a great slidecast.)
Pangeanic Cor-ActivaTM-Neural machine translation Taus Tokyo 2017Manuel Herranz
Presentation of Pangeanic language technologies as a result of EU and national R&D: Cor for web crawling and website translation, linked to Elastic Search-based ActivaTM and NeuralMT
This paper presents the method of applying speaker-independent and bidirectional speech-to-speech translation system for spontaneous dialogs in real time calling system. This technique recognizes spoken input, analyzes and translates it, and finally utters the translation. The major part of Speech translation comes under Natural language processing. Natural language processing is a branch of Artificial Intelligence that deals with analyzing, understanding and generating the languages that humans use naturally in order to interface with computers in both written and spoken contexts using natural human languages instead of computer languages. Speech Translation involves techniques to translate the spoken sentences from one language to another. The major part of speech translation involves Speech Recognition which is the translation of spoken speech to text and identifying the context and linguistic structure of the input speech. In the current scenario, the machine does not identify whether the given word is in past tense or present tense. By using the algorithm, we search for a word to check if it is past or present by searching for the sub strings, as “ed”, ”had”, ”Done”, etc., This paper gives us an idea on working with API’s to translate the input speech to the required output speech and thus increasing the efficiency of Speech Translation in cellular devices and also a mobile application that will help us to monitor all the audios present in mobile device and translate it into required language.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Full-RAG: A modern architecture for hyper-personalization
Language Grid
1. UMD Crowdsourcing and Translation Workshop, June 10-11 Language Grid: Service-OrientedInfrastructurefor Multilingual Society Donghui Lin National Institute of Information and Communications Technology (NICT), Japan
3. Language Grid Architecture Education Medical Care Disaster Management more more Sharing Multilingual Information Translation Services at Hospital Receptions Universal Playground Language Support for Multicultural Societies Sharing language resources such as dictionaries and machine translators around the world German Research Center for Artificial Intelligence Kookmin University Stuttgart University National Institute of Informatics Princeton University National Research Council, Italy Chinese Academy of Sciences Google Inc. NICT NTT Research Labs Asian Disaster Reduction Center NECTEC Univ. of Indonesia 3
4. From Language Resources to Language Services Dictionary Service 避難場所 Wrapping disaster shelter Dictionary Return translated word Dictionary Service Parallel Text Service 避難場所は、家から近い学校です。 Wrapping In the case of disaster, people should be evacuated to a school nearby their house. Return similar translated text Parallel Text Parallel Text Service Machine Translation Service 避難場所は、家から近い学校です。 Wrapping Disaster shelter is school close from a house. Translate by machine Machine Translator Machine Translation Service Human Translation Service 避難場所は、家から近い学校です。 Wrapping Your disaster shelter is the school closest to your house. Translate with high quality Human Translator Human Translation Service
5. From Atomic Services to Composite Services 授業料は無償ですが、教材費や給食費は必要です。 (Original Text in English:Tuition is free,but cost of textbooks and school meals is required) Multilingual Service Infrastructure Multilingual Communication Support System Over60 language services are available currently (April 2010) AutoComplete Service Multilingual Communication Support System for a middle school in Kawasaki City, Japan School Guidance for Foreign Guardians Mie Prefecture, JapanParallel Text As aulas referentes ao ensino obrigatório são gratuitas. Porém, serão cobrados os valores referentes à refeição escolar e os materiais a serem usados. (Translation Result in English: Tuition of compulsory education is free,but cost of textbooks and school meals is required) If original text is registered as parallel text item, perfect translation can be acquired Composite Translation Service A taxa escolar é gratuita, mas a um custo de material educativo e um gasto de merenda escolar são necessários. (Translation Result in English: Although tuition is free,cost of textbooks and waste of school meals is necessary.) Display area for conversationlogs J-Server, Kodensha (Rule-based Translation) Translation(ja-en) Multi-hop Translation Web-transer, CrossLanguage (Rule-based Translation) Translation(en-pt) Translation quality is similar to that of free translation on the Web ・Multi-hop translation ・Composite translation with dictionary ・Best translation selection A taxa escolar é gratuita, mas as taxas de materiais didáticos e merenda escolar são as taxas necessárias. (Translation Result in English: Although tuition is free,the rate that learns materials and school meals is the necessary cost. ) Composite Translation with Dictionary Mecab, NTT Morphological Analysis Professional translation can be realized by combining dictionaries fortechnical terminology (jargon) TreeTagger, University of Stuttgart Morphological Analysis Education Dictionary, The Toyota Foundation Dictionary Best translation result can be selected from multiple translation services AutoComplete Service Composite Translation with Dictionary Best Translation Selection Service O ensino é livre, as taxas de materiais de aprendizagem taxas de merenda escolar e é necessário. (Translation Result in English: Tuition is free,but cost of textbooks and school feeding is required.) Translation quality is possible to be further improved by learning from examples (parallel texts). (Under development by Kyoto Univ.) GoogleTranslate, Google (Statistical Translation) Translation(ja-pt) Best Translation Selection
8. Kyoto Univ. (Japan), Shanghai Jiao Tong Univ. (China), Univ. of Stuttgart (Germany), IT Univ. of Copenhagen (Denmark), Princeton Univ. (USA), DFKI (Germany), CNR (Italy), Chinese Academy of Sciences (China), NECTEC (Thailand), and more.
21. Participatory Design Approach Users create and share their own language services, combine their services with services on the Language Grid to support their multilingual activities Multilingual medical support system in a hospital Multilingual chatting system in a middle school Multilingual BBS for international staffs in an NPO
22. Participatory Design Approach Users customize how to use language services on the Language Grid to support their multilingual activities User can customize translation services and dictionary services Multilingual BBS
38. Open Smart Classroom (IEEE TKED 2009) Language Grid YueSuo, Naoki Miyata, Hiroki Morikawa, Toru Ishida and Yuanchun Shi. Open Smart Classroom: Extensible and Scalable Learning System in Smart Space using Web Service Technology. IEEE Transactions on Knowledge and Data Engineering, Vol.21, No.6, pp. 814-828, 2009. Gold Prize (1st place) in "Microsoft Cup" IEEE China Student Paper Contest.
39. Analysis of Multicultural Communication (CSCW 2006, CHI 2009) Problems of MT-mediated communication Translation Asymmetry Echoing for ratification does not work. Naomi Yamashita, Reiko Inaba, Hideaki Kuzuoka and Toru Ishida. Difficulties in Establishing Common Ground in Multiparty Groups using Machine Translation. International Conference on Human Factors in Computing Systems (CHI-09), pp. 679-688, 2009. Naomi Yamashita and Toru Ishida. Effects of Machine Translation on Collaborative Work. International Conference on Computer Supported Cooperative Work (CSCW-06), pp. 515-523, 2006.
40. Cross Language News Analysis (ongoing) Translation andDictionary Creation バラク・オバマ 巴拉克·奥巴马 Japan China Korea USA World News Analysis Prof. Yoshioka, Hokkaido University, Japan
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42. Wiki-to-Wiki Translation (just started) Number of Wikipedia articles by language Support Wikipedia communities to create multilingual articles. Number of Wiktionary entries by language 15
45. Morphological Analysis Technical Term Extraction Technical Term Bilingual Dictionary Technical Term Bilingual Dictionary Term Replacement Machine Translation Term Replacement + Any remaining terms Any remaining terms Constraint-basedHorizontal Service Composition (ISWC 2006) Original sentence X1 Vertical service composition is to create a workflow. Horizontal service composition is to select atomic services for a given workflow. Satisfy hard constraints (required functions), while maximizing soft constraints (QoS for example). Set of morphemes X2 Set of technical terms included in the sentence Set of technical terms included in the sentence No No yes yes X3 Intermediate code of technical term Translation of technical term Original sentence Set of technical terms Set of intermediate code X4 Sentence including intermediate code. X5 Translated sentence Set of intermediate code Set of term translations X6 Ahlem Ben Hassine, Matsubara Shigeo and Toru Ishida. Constraint-based Approach for Web Service Composition. International Semantic Web Conference (ISWC-06), pp. 130-143, 2006.
46. Summary: Research Issues Interaction analysis Creating conversational common ground with inconsistent, asymmetric, and intransitive machine translations. (CSCW2006, CHI2009) Service Composition Horizontal service composition. (ISWC2006, ICWS2008) Service supervision. (ICWS2009, SCC2010) Context aware service composition for pivot translations. (IJCAI2009) Provider-centered trust for autonomic service composition. Pricing composite/atomic language services. User-centered QoS for composite language services. Customizing statistical translations with community dictionaries. Extending the Language Grid Emotions and pictograms in language services (ESWC2008) e-learning grid (IEEE Transactions on KDE, 2009) 19
50. 23 MT-Mediated Communication (CSCW06, CHI09) To observe and analyze the effects of machine translation on communication among monolinguals Participants: 5 Chinese-Japanesepairs, 3 Japanese-Korean pairs They have never met before the experiments Tasks: Each pair of users try to indentify the same avatars by using their mother tongues and machine translations Communication medias: MT-based multilingual chatting systems
51. 24 Experiment Design Indentify the same avatars < China Side> < Japan Side> Multilingual Chatting System Based on the Language Grid
52. Analysis of MT-Mediated Communication Problem of MT-mediated communication (1) Translation Asymmetry Echoing for ratification does not work.
53. Analysis of MT-Mediated Communication Problem of MT-mediated communication (2) Translation Inconsistency Different expressions of a same sentence can get totally different translation results.
54. MT-Mediated Collaborative Translation Protocols (IUI2009) Language Grid Basic concept of collaborative translation DaisukeMorita and Toru Ishida. Collaborative Translation by Monolinguals with Machine Translators. International Conference on Intelligent User Interfaces (IUI-09), pp. 361-365, 2009.
58. Language Grid for Multilingual Localization (ICSOC2009, LREC2010) Machine translation VS Human translation Human translations are of high costs with long durations Machine translation have limited translation quality (in dimensions of fluency and adequacy) Language Grid improves traditional machine translation service by combining community dictionaries, but still has limitation in translation quality Language Grid for Localization Processes Localization processes requires perfect translation quality that machine translation services cannot provide 31 Let’s try to combine machine translation services and human activities using the Language Grid!!
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60. Experiment Design Localization Contents Business, University, Temple… Languages JA, EN, ZH-CN, ZH-TW, KO, PT, ES, DE, FR… Monolingual Foreign students in Kyoto University Bilingual Professional bilingual translator in Translation Company Machine Translation Services Composite translation with bilingual dictionary For each content, there is a dictionary covers 15%-25% of the total contents Comparison Localization Process by using Language Grid Common Localization Process
63. Monolingual Modification Rate and Translation Time Monolingual modification time keeps stable for different modification rate Bilingual translation/confirmation time becomes less when monolingual modification rate increases Bilingual Time Monolingual Time
65. Monolingual Modification Rate and Bilingual Time Reduction Rate The bilingual time reduction rate increases with the monolingual modification rate
66. Challenging Issues Requirements of composing machine translation and human Methodologies of balancing the cost and quality Control of human translation qualities and machine translation quality Interaction design of human-human and human-machine translation