How do Words Contribute to Sentence Semantics? Revisiting Sentence Embeddings with a Perturbation Method
Wenlin Yao, Lifeng Jin, Hongming Zhang, Xiaoman Pan, Kaiqiang Song, Dian Yu, Dong Yu, Jianshu Chen
Abstract
Understanding sentence semantics requires an interpretation of the main information from a concrete context. To investigate how individual word contributes to sentence semantics, we propose a perturbation method for unsupervised semantic analysis. We next re-examine SOTA sentence embedding models’ ability to capture the main semantics of a sentence by developing a new evaluation metric to adapt sentence compression datasets for automatic evaluation. Results on three datasets show that unsupervised discourse relation recognition can serve as a general inference task that can more effectively aggregate information to essential contents than several SOTA unsupervised sentence embedding models.- Anthology ID:
- 2023.eacl-main.218
- Volume:
- Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3001–3010
- Language:
- URL:
- https://aclanthology.org/2023.eacl-main.218
- DOI:
- Cite (ACL):
- Wenlin Yao, Lifeng Jin, Hongming Zhang, Xiaoman Pan, Kaiqiang Song, Dian Yu, Dong Yu, and Jianshu Chen. 2023. How do Words Contribute to Sentence Semantics? Revisiting Sentence Embeddings with a Perturbation Method. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3001–3010, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- How do Words Contribute to Sentence Semantics? Revisiting Sentence Embeddings with a Perturbation Method (Yao et al., EACL 2023)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2023.eacl-main.218.pdf