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Table of contents
Abbreviations
1 Introduction
1.1 Human-Machine Collaboration in Machine Translation
1.2 Towards a New Protocol for Improved Human-Machine Collaboration
1.3 Automatic Translation of Cochrane Medical Review Abstracts
1.4 Contributions
1.5 Thesis Outline
2 A Statistical Machine Translation Primer
2.1 Basic Principles and System Types
2.2 Phrase-Based Statistical Machine Translation
2.2.1 Formal Definition
2.3 The Translation Model
2.3.1 Word Alignments
2.3.2 Phrase-Table Building
2.3.3 Reordering Models
2.4 The Language Model
2.5 Scoring
2.6 Decoding
2.7 Automatic Evaluation
2.8 Summary
3 Human-Machine Collaboration in Statistical Machine Translation
3.1 Injection of Human Knowledge
3.1.1 Post-Edition
3.1.2 Pre-Edition
3.1.3 Quality Estimation and its Role in Post- and Pre-Edition
3.2 Interactive Machine Translation
3.3 Exploitation of Human Knowledge
3.3.1 Online Adaptation
3.3.2 Selectiveness towards Human Feedback (Active Learning)
3.3.3 Domain Adaptation
3.3.4 Automatic Post-Editing
3.4 Computer-Assisted Translation Systems
3.5 Summary
4 Diagnosing High-Quality Statistical Machine Translation within the Cochrane Context
4.1 Human Evaluation and Error Analysis
4.2 Automatic Evaluation and Error Analysis
4.3 Automatic Translation of Cochrane Review Abstracts
4.3.1 Cochrane Production Context and Corpus
4.3.2 Manual Error Analysis of Post-Edits
4.3.3 Cochrane High-Quality Statistical Machine Translation System
4.3.4 Methodology for Diagnosing High-Quality Machine Translation
4.3.5 Results and Analysis
4.4 Summary
5 Detection of Translation Difficulties
5.1 Methodology
5.1.1 Gold Annotations and Segmentations
5.1.2 Main Features
5.1.3 Classification Algorithms
5.2 Detection of Difficulties as a Classification Problem
5.3 Intrinsic Evaluation: Experiments in the MEDICAL domain
5.3.1 Data and Systems
5.3.2 Choice of the Classification Algorithm
5.3.3 Classifier Feature Evaluation
5.4 Intrinsic Evaluation: Experiments in the UN domain
5.4.1 Features
5.4.2 Data
5.4.3 System Building
5.4.4 Source Translation Difficulty Analysis
5.4.5 Classifier Feature Evaluation
5.5 Summary
6 Resolution of Translation Difficulties with Human Help
6.1 Pre-Edition vs. Post-Edition
6.2 Human-Assisted Machine Translation Protocol
6.3 Evaluation of Pre-Translation
6.4 HAMT: a Sentence-Level Scenario
6.5 Experiments in a Simulated Setting for MEDICAL
6.5.1 Comparison to Post-Edition
6.6 Experiments in a Simulated Setting for UN
6.7 HAMT: a Document-Level Approach
6.7.1 Document-Level Human-Assisted Machine Translation
6.7.2 Selection of Crucial Difficult-to-Translate Segments
6.7.3 Update of Translation Models
6.7.4 Cochrane Abstracts: Experiments in a Simulated Setting
6.7.5 Cochrane Abstracts: Experiments in a Real-life Setting
6.8 Summary
7 Conclusion and Perspectives
7.1 Contributions
7.2 Perspectives
Appendix A Extracts from the Cochrane Corpus
A.1 Cochrane Reference Corpus
A.2 Cochrane Post-editing Corpus 1
Appendix B Extracts of Cochrane API Code
Appendix C Extracts of Cochrane UI Code
Appendix D Examples of Medical Text Challenges
Appendix E Standard Features for Translation Difficulty Detection
E.1 List of word-level standard features
E.2 List of standard phrase-level features
Appendix F Feature Ablation Experiments
Appendix G Examples of the Impact on the Context
Appendix H Cochrane Review Abstract Pre- and Post-Edited by Humans
Appendix I Publications by the Author
Bibliography
