Improving Word Sense Disambiguation with Translations

Yixing Luan, Bradley Hauer, Lili Mou, Grzegorz Kondrak


Abstract
It has been conjectured that multilingual information can help monolingual word sense disambiguation (WSD). However, existing WSD systems rarely consider multilingual information, and no effective method has been proposed for improving WSD by generating translations. In this paper, we present a novel approach that improves the performance of a base WSD system using machine translation. Since our approach is language independent, we perform WSD experiments on several languages. The results demonstrate that our methods can consistently improve the performance of WSD systems, and obtain state-ofthe-art results in both English and multilingual WSD. To facilitate the use of lexical translation information, we also propose BABALIGN, an precise bitext alignment algorithm which is guided by multilingual lexical correspondences from BabelNet.
Anthology ID:
2020.emnlp-main.332
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4055–4065
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.332
DOI:
10.18653/v1/2020.emnlp-main.332
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.332.pdf