@inproceedings{luan-etal-2020-improving,
title = "Improving Word Sense Disambiguation with Translations",
author = "Luan, Yixing and
Hauer, Bradley and
Mou, Lili and
Kondrak, Grzegorz",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.332",
doi = "10.18653/v1/2020.emnlp-main.332",
pages = "4055--4065",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Improving Word Sense Disambiguation with Translations
%A Luan, Yixing
%A Hauer, Bradley
%A Mou, Lili
%A Kondrak, Grzegorz
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F luan-etal-2020-improving
%X 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.
%R 10.18653/v1/2020.emnlp-main.332
%U https://aclanthology.org/2020.emnlp-main.332
%U https://doi.org/10.18653/v1/2020.emnlp-main.332
%P 4055-4065
Markdown (Informal)
[Improving Word Sense Disambiguation with Translations](https://aclanthology.org/2020.emnlp-main.332) (Luan et al., EMNLP 2020)
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.