Machine Translation From Real Users to Research Lecture Notes in Computer Science 3265 Lecture Notes in Artificial Intelligence 6th edition by Jeffrey Allen, Robert Frederking, Kathryn Taylor ISBN 3540233008  978-3540233008

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ISBN 10: 3540233008
ISBN 13: 978-3540233008
Author:  Jeffrey Allen, Robert Frederking, Kathryn Taylor 

The previous conference in this series (AMTA 2002) took up the theme “From Research to Real Users”, and sought to explore why recent research on data-driven machine translation didn’t seem to be moving to the marketplace. As it turned out, the ?rst commercial products of the data-driven research movement were just over the horizon, andintheinterveningtwoyearstheyhavebeguntoappearinthemarketplace. Atthesame time,rule-basedmachinetranslationsystemsareintroducingdata-driventechniquesinto the mix in their products. Machine translation as a software application has a 50-year history. There are an increasing number of exciting deployments of MT, many of which will be exhibited and discussed at the conference. But the scale of commercial use has never approached the estimates of the latent demand. In light of this, we reversed the question from AMTA 2002, to look at the next step in the path to commercial success for MT. We took user needs as our theme, and explored how or whether market requirements are feeding into research programs. The transition of research discoveries to practical use involves te- nicalquestionsthatarenotassexyasthosethathavedriventheresearchcommunityand research funding. Important product issues such as system customizability, computing resource requirements, and usability and ?tness for particular tasks need to engage the creativeenergiesofallpartsofourcommunity,especiallyresearch,aswemovemachine translation from a niche application to a more pervasive language conversion process. Thesetopicswereaddressedattheconferencethroughthepaperscontainedinthesep- ceedings, and even more speci?cally through several invited presentations and panels.

Machine Translation: From Real Users to Research (Lecture Notes in Computer Science 3265 Lecture Notes in Artificial Intelligence) 6th Table of contents:

Preface

• Introduction to Machine Translation
• The Importance of User-Centered Approaches in MT
• Structure and Aims of the Book

Chapter 1: Overview of Machine Translation

• Definition and Historical Evolution of MT
• Key Approaches in MT: Rule-Based, Statistical, Neural Networks
• The Role of MT in Modern Language Processing
• MT in Multilingual Communication

Chapter 2: User-Centered Machine Translation

• The Concept of Usability in MT Systems
• User Expectations and Requirements for MT
• Case Studies of MT in Real-World Applications
• Impact of MT on Global Communication and Industry

Chapter 3: MT Evaluation: From Research to Practice

• Evaluation Frameworks for MT Systems
• Objective vs. Subjective Evaluation
• User Feedback in MT Evaluation
• Performance Metrics: BLEU, TER, METEOR, and Beyond

Chapter 4: Building User-Friendly MT Systems

• Designing MT Systems with End Users in Mind
• Usability Testing and User Experience Design for MT
• Personalization and Adaptability in MT Systems
• Integration of MT in Software Tools and Platforms

Chapter 5: Advances in Statistical and Neural Machine Translation

• Introduction to Statistical Machine Translation (SMT)
• Neural Machine Translation (NMT) Overview
• Techniques in NMT: Deep Learning, Sequence-to-Sequence Models
• Hybrid Approaches: Combining SMT and NMT

Chapter 6: Challenges and Limitations in Machine Translation

• Linguistic Challenges: Ambiguity, Idioms, and Polysemy
• Cultural and Contextual Issues in MT
• Low-Resource Languages and Data Scarcity
• Ethical Considerations in MT

Chapter 7: Machine Translation in Specific Domains

• MT for Scientific and Technical Texts
• MT in Legal, Medical, and Business Domains
• Adapting MT for Domain-Specific Terminology and Style
• Case Studies of Domain-Specific MT Applications

Chapter 8: Machine Translation and Human Collaboration

• Post-Editing and Human-Assisted MT
• MT for Translation Workflows in Professional Environments
• Human vs. Machine: The Future of Translation in the Workforce
• Collaboration Tools in Translation Management Systems

Chapter 9: Research Directions and Emerging Trends in MT

• Current Trends in Neural Networks and Deep Learning for MT
• Cross-lingual Transfer Learning
• MT for Multimodal Content (e.g., Audio, Images)
• The Future of MT in Artificial Intelligence and Automation

Chapter 10: Conclusion and Future of Machine Translation

• Summary of Key Insights from Research and Users
• The Role of MT in a Globalized, Multilingual World
• Ethical, Social, and Technological Considerations for the Future of MT
• The Road Ahead: From Research to Commercial Applications

Appendices

• Further Reading on Machine Translation
• Glossary of MT Terms and Acronyms
• Resources for MT Research and Development
• Index

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