Contents
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10.1 Introduction 10.1 Introduction
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10.2 SFST Training and Decoding 10.2 SFST Training and Decoding
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10.2.1 Word Alignment 10.2.1 Word Alignment
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10.2.2 Bilanguage Representation 10.2.2 Bilanguage Representation
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10.2.3 Bilingual Phrase Acquisition and Local Reordering 10.2.3 Bilingual Phrase Acquisition and Local Reordering
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10.2.4 SFST Model 10.2.4 SFST Model
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10.2.5 Decoding 10.2.5 Decoding
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10.2.6 Word Insertion Model 10.2.6 Word Insertion Model
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10.2.7 Global Reordering 10.2.7 Global Reordering
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10.3 Discriminant Models for Lexical Selection 10.3 Discriminant Models for Lexical Selection
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10.3.1 Sequential Lexical Choice Model 10.3.1 Sequential Lexical Choice Model
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10.3.2 Bag-of-Words Lexical Choice Model 10.3.2 Bag-of-Words Lexical Choice Model
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10.4 Choosing the Classifier 10.4 Choosing the Classifier
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10.4.1 Multiclass vs. Binary Classification 10.4.1 Multiclass vs. Binary Classification
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10.4.2 Geometric vs. Probabilistic Interpretation 10.4.2 Geometric vs. Probabilistic Interpretation
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10.4.3 L1 vs. L2 Regularization 10.4.3 L1 vs. L2 Regularization
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10.5 Data and Experiments 10.5 Data and Experiments
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10.5.1 UN and Hansard Corpora 10.5.1 UN and Hansard Corpora
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10.6 Discussion 10.6 Discussion
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10.7 Conclusions 10.7 Conclusions
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10 Statistical Machine Translation through Global Lexical Selection and Sentence Reconstruction
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Published:November 2008
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Abstract
This chapter presents the details of training a global lexical selection model using classification techniques and sentence reconstruction models using permutation automata. It also provides a stochastic finite-state transducer (SFST) as an example of an approach that relies on local associations, and uses it to compare and contrast the approach. The chapter is organized as follows. Section 10.2 describes in detail the different stages used to train an SFST translation model, and discusses the steps in decoding a source input using the trained SFST model. Section 10.3 presents the global lexical selection and the sentence reconstruction models. Section 10.4 discusses the rationale for choosing the classifier to train the global lexical selection model. Section 10.5 reports the results of the two translation models on a few data sets and contrasts the strengths and limitations of the two approaches.
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