Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Nils Reimers, Iryna Gurevych


Abstract
BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10,000 sentences requires about 50 million inference computations (~65 hours) with BERT. The construction of BERT makes it unsuitable for semantic similarity search as well as for unsupervised tasks like clustering. In this publication, we present Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT. We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning tasks, where it outperforms other state-of-the-art sentence embeddings methods.
Anthology ID:
D19-1410
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3982–3992
Language:
URL:
https://aclanthology.org/D19-1410
DOI:
10.18653/v1/D19-1410
Bibkey:
Cite (ACL):
Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3982–3992, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (Reimers & Gurevych, EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1410.pdf
Code
 UKPLab/sentence-transformers +  additional community code
Data
GLUEMPQA Opinion CorpusMultiNLISICKSNLISSTSTS BenchmarkSemantic Textual Similarity (2012 - 2016)SentEval