Abstract
We address the task of unsupervised Semantic Textual Similarity (STS) by ensembling diverse pre-trained sentence encoders into sentence meta-embeddings. We apply, extend and evaluate different meta-embedding methods from the word embedding literature at the sentence level, including dimensionality reduction (Yin and Schütze, 2016), generalized Canonical Correlation Analysis (Rastogi et al., 2015) and cross-view auto-encoders (Bollegala and Bao, 2018). Our sentence meta-embeddings set a new unsupervised State of The Art (SoTA) on the STS Benchmark and on the STS12-STS16 datasets, with gains of between 3.7% and 6.4% Pearson’s r over single-source systems.- Anthology ID:
- 2020.acl-main.628
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7027–7034
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.628
- DOI:
- 10.18653/v1/2020.acl-main.628
- Cite (ACL):
- Nina Poerner, Ulli Waltinger, and Hinrich Schütze. 2020. Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7027–7034, Online. Association for Computational Linguistics.
- Cite (Informal):
- Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity (Poerner et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/landing_page/2020.acl-main.628.pdf
- Data
- SentEval