
Contents
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35.1 Introduction 35.1 Introduction
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35.2 History 35.2 History
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35.3 Rule-Based MT 35.3 Rule-Based MT
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35.3.1 Direct RBMT Approach 35.3.1 Direct RBMT Approach
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35.3.2 Transfer RBMT Approach 35.3.2 Transfer RBMT Approach
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35.3.3 Interlingua RBMT Approach 35.3.3 Interlingua RBMT Approach
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35.4 Example-based MT 35.4 Example-based MT
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35.5 Statistical MT 35.5 Statistical MT
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35.5.1 Phrase-Based SMT 35.5.1 Phrase-Based SMT
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35.5.1.1 Word alignment 35.5.1.1 Word alignment
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35.5.1.2 Phrase extraction and scoring 35.5.1.2 Phrase extraction and scoring
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35.5.1.3 Language model 35.5.1.3 Language model
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35.5.1.4 Reordering models 35.5.1.4 Reordering models
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35.5.1.5 Decoding 35.5.1.5 Decoding
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35.5.1.6 Parameter tuning 35.5.1.6 Parameter tuning
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35.5.2 Tree-Based SMT 35.5.2 Tree-Based SMT
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35.5.2.1 Hierarchical PBSMT 35.5.2.1 Hierarchical PBSMT
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35.5.2.2 Syntax-based SMT 35.5.2.2 Syntax-based SMT
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35.5.3 Other Types of Linguistic Information for SMT 35.5.3 Other Types of Linguistic Information for SMT
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35.6 Quality Evaluation and Estimation 35.6 Quality Evaluation and Estimation
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35.6.1 BLEU 35.6.1 BLEU
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35.6.2 METEOR 35.6.2 METEOR
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35.6.3 TER/HTER 35.6.3 TER/HTER
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35.6.4 Linguistically Informed Metrics 35.6.4 Linguistically Informed Metrics
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35.6.5 Quality Estimation Metrics 35.6.5 Quality Estimation Metrics
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35.7 Remarks and Perspectives 35.7 Remarks and Perspectives
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Further Reading and Relevant Resources Further Reading and Relevant Resources
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Acknowledgements Acknowledgements
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References References
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35 Machine Translation
Get accessLucia Specia is Professor of Natural Language Processing at Imperial College London and the University of Sheffield. Her research focuses on various aspects of data-driven approaches to language processing, with a particular interest in multimodal and multilingual context models. Her work has been applied to various tasks such as machine translation, image captioning, quality estimation, and text adaptation. She is the recipient of the MultiMT ERC Starting Grant on multimodal machine translation.
Yorick Wilks is Emeritus Professor of Artificial Intelligence at the University of Sheffield, and a Senior Research Scientist at IHMC, the Institute for Human and Machine Cognition, in Florida. His most recent book is Close Encounters with Artificial Companions (Benjamins, 2010). In 2008 he was awarded the Zampolli Prize and the ACL Lifetime Achievement Award. In 2009 he was awarded the Lovelace Medal by the British Computer Society. In 2009 he was elected a Fellow of the ACM.
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Published:07 March 2016
Cite
Abstract
Machine Translation (MT) is and always has been a core application in the field of natural-language processing. It is a very active research area and it has been attracting significant commercial interest, most of which has been driven by the deployment of corpus-based, statistical approaches, which can be built in a much shorter time and at a fraction of the cost of traditional, rule-based approaches, and yet produce translations of comparable or superior quality. This chapter aims at introducing MT and its main approaches. It provides a historical overview of the field, an introduction to different translation methods, both rationalist (rule-based) and empirical, and a more in depth description of state-of-the-art statistical methods. Finally, it covers popular metrics to evaluate the output of machine translation systems.
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