@inproceedings{tang-etal-2016-improving-translation,
title = "Improving Translation Selection with Supersenses",
author = "Tang, Haiqing and
Xiong, Deyi and
Lopez de Lacalle, Oier and
Agirre, Eneko",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1293",
pages = "3114--3123",
abstract = "Selecting appropriate translations for source words with multiple meanings still remains a challenge for statistical machine translation (SMT). One reason for this is that most SMT systems are not good at detecting the proper sense for a polysemic word when it appears in different contexts. In this paper, we adopt a supersense tagging method to annotate source words with coarse-grained ontological concepts. In order to enable the system to choose an appropriate translation for a word or phrase according to the annotated supersense of the word or phrase, we propose two translation models with supersense knowledge: a maximum entropy based model and a supersense embedding model. The effectiveness of our proposed models is validated on a large-scale English-to-Spanish translation task. Results indicate that our method can significantly improve translation quality via correctly conveying the meaning of the source language to the target language.",
}
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<abstract>Selecting appropriate translations for source words with multiple meanings still remains a challenge for statistical machine translation (SMT). One reason for this is that most SMT systems are not good at detecting the proper sense for a polysemic word when it appears in different contexts. In this paper, we adopt a supersense tagging method to annotate source words with coarse-grained ontological concepts. In order to enable the system to choose an appropriate translation for a word or phrase according to the annotated supersense of the word or phrase, we propose two translation models with supersense knowledge: a maximum entropy based model and a supersense embedding model. The effectiveness of our proposed models is validated on a large-scale English-to-Spanish translation task. Results indicate that our method can significantly improve translation quality via correctly conveying the meaning of the source language to the target language.</abstract>
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%0 Conference Proceedings
%T Improving Translation Selection with Supersenses
%A Tang, Haiqing
%A Xiong, Deyi
%A Lopez de Lacalle, Oier
%A Agirre, Eneko
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 dec
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F tang-etal-2016-improving-translation
%X Selecting appropriate translations for source words with multiple meanings still remains a challenge for statistical machine translation (SMT). One reason for this is that most SMT systems are not good at detecting the proper sense for a polysemic word when it appears in different contexts. In this paper, we adopt a supersense tagging method to annotate source words with coarse-grained ontological concepts. In order to enable the system to choose an appropriate translation for a word or phrase according to the annotated supersense of the word or phrase, we propose two translation models with supersense knowledge: a maximum entropy based model and a supersense embedding model. The effectiveness of our proposed models is validated on a large-scale English-to-Spanish translation task. Results indicate that our method can significantly improve translation quality via correctly conveying the meaning of the source language to the target language.
%U https://aclanthology.org/C16-1293
%P 3114-3123
Markdown (Informal)
[Improving Translation Selection with Supersenses](https://aclanthology.org/C16-1293) (Tang et al., COLING 2016)
ACL
- Haiqing Tang, Deyi Xiong, Oier Lopez de Lacalle, and Eneko Agirre. 2016. Improving Translation Selection with Supersenses. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3114–3123, Osaka, Japan. The COLING 2016 Organizing Committee.