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Journal ArticleDOI

Hierarchical Phrase-Based Translation

David Chiang
- 01 Jun 2007 - 
- Vol. 33, Iss: 2, pp 201-228
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TLDR
A statistical machine translation model that uses hierarchical phrasesphrases that contain subphrasing that is formally a synchronous context-free grammar but is learned from a parallel text without any syntactic annotations is presented.
Abstract
We present a statistical machine translation model that uses hierarchical phrases---phrases that contain subphrases. The model is formally a synchronous context-free grammar but is learned from a parallel text without any syntactic annotations. Thus it can be seen as combining fundamental ideas from both syntax-based translation and phrase-based translation. We describe our system's training and decoding methods in detail, and evaluate it for translation speed and translation accuracy. Using BLEU as a metric of translation accuracy, we find that our system performs significantly better than the Alignment Template System, a state-of-the-art phrase-based system.

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Citations
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A Simple, Fast, and Effective Reparameterization of IBM Model 2

TL;DR: A simple log-linear reparameterization of IBM Model 2 that overcomes problems arising from Model 1’'s strong assumptions and Model 2’s overparameterization is presented.
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Six Challenges for Neural Machine Translation.

TL;DR: The authors explore six challenges for NMT: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search, and show both deficiencies and improvements over the quality of phrase-based statistical machine translation.
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Addressing the Rare Word Problem in Neural Machine Translation

TL;DR: This paper proposed and implemented an effective technique to address the problem of out-of-vocabulary (OOV) word translation in NMT, which trains an NMT system on data that is augmented by the output of a word alignment algorithm, and then uses this information in a post-processing step that translates every OOV word using a dictionary.
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Modeling Coverage for Neural Machine Translation

TL;DR: This paper propose a coverage vector to keep track of the attention history and feed it to the attention model to adjust future attention, which enables NMT system to consider more about untranslated source words.
References
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Book

Introduction to Algorithms

TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Proceedings ArticleDOI

Bleu: a Method for Automatic Evaluation of Machine Translation

TL;DR: This paper proposed a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run.
Proceedings Article

SRILM – An Extensible Language Modeling Toolkit

TL;DR: The functionality of the SRILM toolkit is summarized and its design and implementation is discussed, highlighting ease of rapid prototyping, reusability, and combinability of tools.
Journal Article

The mathematics of statistical machine translation: parameter estimation

TL;DR: The authors describe a series of five statistical models of the translation process and give algorithms for estimating the parameters of these models given a set of pairs of sentences that are translations of one another.
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