Open AccessProceedings Article
Additive Neural Networks for Statistical Machine Translation
Lemao Liu,Taro Watanabe,Eiichiro Sumita,Tiejun Zhao +3 more
- Vol. 1, pp 791-801
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TLDR
The proposed variant of a neural network is employed as the input to the neural network, which encodes each word as a feature vector and outperforms the log-linear translation models with/without embedding features on Chinese- to-English and Japanese-to-English translation tasks.Abstract:
Most statistical machine translation (SMT) systems are modeled using a loglinear framework. Although the log-linear model achieves success in SMT, it still suffers from some limitations: (1) the features are required to be linear with respect to the model itself; (2) features cannot be further interpreted to reach their potential. A neural network is a reasonable method to address these pitfalls. However, modeling SMT with a neural network is not trivial, especially when taking the decoding efficiency into consideration. In this paper, we propose a variant of a neural network, i.e. additive neural networks, for SMT to go beyond the log-linear translation model. In addition, word embedding is employed as the input to the neural network, which encodes each word as a feature vector. Our model outperforms the log-linear translation models with/without embedding features on Chinese-to-English and Japanese-to-English translation tasks.read more
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A Recursive Recurrent Neural Network for Statistical Machine Translation
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Bilingually-constrained Phrase Embeddings for Machine Translation
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Knowledge-Based Semantic Embedding for Machine Translation
TL;DR: This paper builds and formulate a semantic space to connect the source and target languages, and applies it to the sequence-to-sequence framework to propose a Knowledge-Based Semantic Embedding (KBSE) method.
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Optimization for statistical machine translation: A survey
Graham Neubig,Taro Watanabe +1 more
TL;DR: This article surveys 12 years of research on optimization for SMT, from the seminal work on discriminative models and minimum error rate training to the most recent advances, covering a wide variety of optimization algorithms for use in both batch and online optimization.
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Bilingual continuous-space language model growing for statistical machine translation
TL;DR: A novel neural network based bilingual LM growing method that enables us to use bilingual parallel corpus for LM growing in SMT and shows that the new method outperforms the existing approaches on both SMT performance and computational efficiency significantly.
References
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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.
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Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
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Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
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A neural probabilistic language model
TL;DR: The authors propose to learn a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences, which can be expressed in terms of these representations.
Proceedings ArticleDOI
Moses: Open Source Toolkit for Statistical Machine Translation
Philipp Koehn,Hieu Hoang,Alexandra Birch,Chris Callison-Burch,Marcello Federico,Nicola Bertoldi,Brooke Cowan,Wade Shen,C. Corbett Moran,Richard Zens,Chris Dyer,Ondrej Bojar,Alexandra Elena Constantin,Evan Herbst +13 more
TL;DR: An open-source toolkit for statistical machine translation whose novel contributions are support for linguistically motivated factors, confusion network decoding, and efficient data formats for translation models and language models.