Sentence-BERT (SBERT) Algorithm

From GM-RKB
(Redirected from SBERT)
Jump to navigation Jump to search

A Sentence-BERT (SBERT) Algorithm is a sentence embedding algorithm that modifies a pre-trained BERT model through the use of Siamese and triplet network structures.



References

2024

2023

2019

  • (Reimers & Gurevych, 2019) ⇒ Nils Reimers, and Iryna Gurevych. (2019). “[https://arxiv.org/pdf/1908.10084.pdf Sentence-BERT: Sentence Embeddings Using Siamese BERT-networks.” arXiv preprint arXiv:1908.10084
    • 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.