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://aclanthology.org/2020.emnlp-main.332
- DOI:
- 10.18653/v1/2020.emnlp-main.332
- Cite (ACL):
- Yixing Luan, Bradley Hauer, Lili Mou, and Grzegorz Kondrak. 2020. Improving Word Sense Disambiguation with Translations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4055–4065, Online. Association for Computational Linguistics.
- Cite (Informal):
- Improving Word Sense Disambiguation with Translations (Luan et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2020.emnlp-main.332.pdf