@inproceedings{xie-zeng-2021-mixture,
title = "A Mixture-of-Experts Model for Antonym-Synonym Discrimination",
author = "Xie, Zhipeng and
Zeng, Nan",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.71",
doi = "10.18653/v1/2021.acl-short.71",
pages = "558--564",
abstract = "Discrimination between antonyms and synonyms is an important and challenging NLP task. Antonyms and synonyms often share the same or similar contexts and thus are hard to make a distinction. This paper proposes two underlying hypotheses and employs the mixture-of-experts framework as a solution. It works on the basis of a divide-and-conquer strategy, where a number of localized experts focus on their own domains (or subspaces) to learn their specialties, and a gating mechanism determines the space partitioning and the expert mixture. Experimental results have shown that our method achieves the state-of-the-art performance on the task.",
}
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%0 Conference Proceedings
%T A Mixture-of-Experts Model for Antonym-Synonym Discrimination
%A Xie, Zhipeng
%A Zeng, Nan
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F xie-zeng-2021-mixture
%X Discrimination between antonyms and synonyms is an important and challenging NLP task. Antonyms and synonyms often share the same or similar contexts and thus are hard to make a distinction. This paper proposes two underlying hypotheses and employs the mixture-of-experts framework as a solution. It works on the basis of a divide-and-conquer strategy, where a number of localized experts focus on their own domains (or subspaces) to learn their specialties, and a gating mechanism determines the space partitioning and the expert mixture. Experimental results have shown that our method achieves the state-of-the-art performance on the task.
%R 10.18653/v1/2021.acl-short.71
%U https://aclanthology.org/2021.acl-short.71
%U https://doi.org/10.18653/v1/2021.acl-short.71
%P 558-564
Markdown (Informal)
[A Mixture-of-Experts Model for Antonym-Synonym Discrimination](https://aclanthology.org/2021.acl-short.71) (Xie & Zeng, ACL-IJCNLP 2021)
ACL
- Zhipeng Xie and Nan Zeng. 2021. A Mixture-of-Experts Model for Antonym-Synonym Discrimination. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 558–564, Online. Association for Computational Linguistics.