Ngoc Quang Luong

Also published as: Ngoc-Quang Luong


2017

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Machine Translation of Spanish Personal and Possessive Pronouns Using Anaphora Probabilities
Ngoc Quang Luong | Andrei Popescu-Belis | Annette Rios Gonzales | Don Tuggener
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We implement a fully probabilistic model to combine the hypotheses of a Spanish anaphora resolution system with those of a Spanish-English machine translation system. The probabilities over antecedents are converted into probabilities for the features of translated pronouns, and are integrated with phrase-based MT using an additional translation model for pronouns. The system improves the translation of several Spanish personal and possessive pronouns into English, by solving translation divergencies such as ‘ella’ vs. ‘she’/‘it’ or ‘su’ vs. ‘his’/‘her’/‘its’/‘their’. On a test set with 2,286 pronouns, a baseline system correctly translates 1,055 of them, while ours improves this by 41. Moreover, with oracle antecedents, possessives are translated with an accuracy of 83%.

2016

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Improving Pronoun Translation by Modeling Coreference Uncertainty
Ngoc Quang Luong | Andrei Popescu-Belis
Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers

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Pronoun Language Model and Grammatical Heuristics for Aiding Pronoun Prediction
Ngoc Quang Luong | Andrei Popescu-Belis
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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A Contextual Language Model to Improve Machine Translation of Pronouns by Re-ranking Translation Hypotheses
Ngoc Quang Luong | Andrei Popescu-Belis
Proceedings of the 19th Annual Conference of the European Association for Machine Translation

2015

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Leveraging Compounds to Improve Noun Phrase Translation from Chinese and German
Xiao Pu | Laura Mascarell | Andrei Popescu-Belis | Mark Fishel | Ngoc-Quang Luong | Martin Volk
Proceedings of the ACL-IJCNLP 2015 Student Research Workshop

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An open-source toolkit for word-level confidence estimation in machine translation
Christophe Servan | Ngoc Tien Le | Ngoc Quang Luong | Benjamin Lecouteux | Laurent Besacier
Proceedings of the 12th International Workshop on Spoken Language Translation: Papers

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Pronoun Translation and Prediction with or without Coreference Links
Ngoc Quang Luong | Lesly Miculicich Werlen | Andrei Popescu-Belis
Proceedings of the Second Workshop on Discourse in Machine Translation

2014

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An efficient two-pass decoder for SMT using word confidence estimation
Ngoc-Quang Luong | Laurent Besacier | Benjamin Lecouteux
Proceedings of the 17th Annual conference of the European Association for Machine Translation

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Word Confidence Estimation for SMT N-best List Re-ranking
Ngoc-Quang Luong | Laurent Besacier | Benjamin Lecouteux
Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation

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LIG System for Word Level QE task at WMT14
Ngoc-Quang Luong | Laurent Besacier | Benjamin Lecouteux
Proceedings of the Ninth Workshop on Statistical Machine Translation

2013

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LIG System for WMT13 QE Task: Investigating the Usefulness of Features in Word Confidence Estimation for MT
Ngoc-Quang Luong | Benjamin Lecouteux | Laurent Besacier
Proceedings of the Eighth Workshop on Statistical Machine Translation

2012

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The LIG English to French machine translation system for IWSLT 2012
Laurent Besacier | Benjamin Lecouteux | Marwen Azouzi | Ngoc Quang Luong
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper presents the LIG participation to the E-F MT task of IWSLT 2012. The primary system proposed made a large improvement (more than 3 point of BLEU on tst2010 set) compared to our last year participation. Part of this improvment was due to the use of an extraction from the Gigaword corpus. We also propose a preliminary adaptation of the driven decoding concept for machine translation. This method allows an efficient combination of machine translation systems, by rescoring the log-linear model at the N-best list level according to auxiliary systems: the basis technique is essentially guiding the search using one or previous system outputs. The results show that the approach allows a significant improvement in BLEU score using Google translate to guide our own SMT system. We also try to use a confidence measure as an additional log-linear feature but we could not get any improvment with this technique.

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Integrating lexical, syntactic and system-based features to improve Word Confidence Estimation in SMT
Ngoc Quang Luong
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 3: RECITAL