Improving Translation Selection with Supersenses

Haiqing Tang, Deyi Xiong, Oier Lopez de Lacalle, Eneko Agirre

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.
Anthology ID:
C16-1293
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
3114–3123
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/C16-1293/
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Improving Translation Selection with Supersenses (Tang et al., COLING 2016)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/C16-1293.pdf
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