EASE: Entity-Aware Contrastive Learning of Sentence Embedding

Sosuke Nishikawa, Ryokan Ri, Ikuya Yamada, Yoshimasa Tsuruoka, Isao Echizen


Abstract
We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities.The advantage of using entity supervision is twofold: (1) entities have been shown to be a strong indicator of text semantics and thus should provide rich training signals for sentence embeddings; (2) entities are defined independently of languages and thus offer useful cross-lingual alignment supervision.We evaluate EASE against other unsupervised models both in monolingual and multilingual settings.We show that EASE exhibits competitive or better performance in English semantic textual similarity (STS) and short text clustering (STC) tasks and it significantly outperforms baseline methods in multilingual settings on a variety of tasks.Our source code, pre-trained models, and newly constructed multi-lingual STC dataset are available at https://github.com/studio-ousia/ease.
Anthology ID:
2022.naacl-main.284
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3870–3885
Language:
URL:
https://aclanthology.org/2022.naacl-main.284
DOI:
10.18653/v1/2022.naacl-main.284
Bibkey:
Cite (ACL):
Sosuke Nishikawa, Ryokan Ri, Ikuya Yamada, Yoshimasa Tsuruoka, and Isao Echizen. 2022. EASE: Entity-Aware Contrastive Learning of Sentence Embedding. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3870–3885, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
EASE: Entity-Aware Contrastive Learning of Sentence Embedding (Nishikawa et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.284.pdf
Code
 studio-ousia/ease
Data
MLDoc