@inproceedings{yao-etal-2023-words,
title = "How do Words Contribute to Sentence Semantics? Revisiting Sentence Embeddings with a Perturbation Method",
author = "Yao, Wenlin and
Jin, Lifeng and
Zhang, Hongming and
Pan, Xiaoman and
Song, Kaiqiang and
Yu, Dian and
Yu, Dong and
Chen, Jianshu",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.218",
doi = "10.18653/v1/2023.eacl-main.218",
pages = "3001--3010",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T How do Words Contribute to Sentence Semantics? Revisiting Sentence Embeddings with a Perturbation Method
%A Yao, Wenlin
%A Jin, Lifeng
%A Zhang, Hongming
%A Pan, Xiaoman
%A Song, Kaiqiang
%A Yu, Dian
%A Yu, Dong
%A Chen, Jianshu
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F yao-etal-2023-words
%X 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.
%R 10.18653/v1/2023.eacl-main.218
%U https://aclanthology.org/2023.eacl-main.218
%U https://doi.org/10.18653/v1/2023.eacl-main.218
%P 3001-3010
Markdown (Informal)
[How do Words Contribute to Sentence Semantics? Revisiting Sentence Embeddings with a Perturbation Method](https://aclanthology.org/2023.eacl-main.218) (Yao et al., EACL 2023)
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.