Learning Continuous Phrase Representations for Translation Modeling

Gao, Jianfeng; He, Xiaodong; Yih, Wen-tau; Deng, Li

doi:10.3115/v1/p14-1066

Cited by 102 publications

(82 citation statements)

References 33 publications

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“…Gao et al (2014a) successfully use an embedding model to refine the estimation of rare phrase-translation probabilities, which is traditionally affected by sparsity problems. Robustness to sparsity is a crucial property of our method, as it allows us to capture context information while avoiding unmanageable growth of model parameters.…”

Section: Related Workmentioning

confidence: 99%

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

Sordoni¹,

Galley²,

Auli³

et al. 2015

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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694

566

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We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances. Our dynamic-context generative models show consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information Retrieval baselines.

show abstract

Section: Related Workmentioning

confidence: 99%

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

Sordoni¹,

Galley²,

Auli³

et al. 2015

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

Self Cite

694

566

View full text Add to dashboard Cite

show abstract

“…Gao et al (2013) also use bag-ofwords but learn BLEU sensitive phrase embeddings. This kind of approaches does not take the word order into account and loses much information.…”

Section: Related Workmentioning

confidence: 99%

Bilingually-constrained Phrase Embeddings for Machine Translation

Zhang

Liu

et al. 2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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We propose Bilingually-constrained Recursive Auto-encoders (BRAE) to learn semantic phrase embeddings (compact vector representations for phrases), which can distinguish the phrases with different semantic meanings. The BRAE is trained in a way that minimizes the semantic distance of translation equivalents and maximizes the semantic distance of nontranslation pairs simultaneously. After training, the model learns how to embed each phrase semantically in two languages and also learns how to transform semantic embedding space in one language to the other. We evaluate our proposed method on two end-to-end SMT tasks (phrase table pruning and decoding with phrasal semantic similarities) which need to measure semantic similarity between a source phrase and its translation candidates. Extensive experiments show that the BRAE is remarkably effective in these two tasks.

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“…However, most of these methods focus on frequent words or an available bilingual phrase table (Zou et al, 2013;Zhang et al, 2014;Gao et al, 2014). Mikolov et al (2013a) learn a global linear projection from source to target using representation of frequent words on both sides.…”

Section: Related Workmentioning

confidence: 99%

Improving Statistical Machine Translation with a Multilingual Paraphrase Database

Seraj

Siahbani

Sarkar

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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The multilingual Paraphrase Database (PPDB) is a freely available automatically created resource of paraphrases in multiple languages. In statistical machine translation, paraphrases can be used to provide translation for out-of-vocabulary (OOV) phrases. In this paper, we show that a graph propagation approach that uses PPDB paraphrases can be used to improve overall translation quality. We provide an extensive comparison with previous work and show that our PPDB-based method improves the BLEU score by up to 1.79 percent points. We show that our approach improves on the state of the art in three different settings: when faced with limited amount of parallel training data; a domain shift between training and test data; and handling a morphologically complex source language. Our PPDB-based method outperforms the use of distributional profiles from monolingual source data.

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Learning Continuous Phrase Representations for Translation Modeling

Cited by 102 publications

References 33 publications

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

Bilingually-constrained Phrase Embeddings for Machine Translation

Improving Statistical Machine Translation with a Multilingual Paraphrase Database

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