2019 SentenceBERTSentenceEmbeddingsU
- (Reimers & Gurevych, 2019) ⇒ Nils Reimers, and Iryna Gurevych. (2019). “Sentence-bert: Sentence Embeddings Using Siamese Bert-networks.” In: arXiv preprint arXiv:1908.10084.
Subject Headings: Sentence-BERT.
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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.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2019 SentenceBERTSentenceEmbeddingsU | Iryna Gurevych Nils Reimers | Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks | 10.48550/arXiv.1908.10084 | 2019 |