Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?

Xilun Chen, Kushal Lakhotia, Barlas Oguz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta, Wen-tau Yih


Abstract
Despite their recent popularity and well-known advantages, dense retrievers still lag behind sparse methods such as BM25 in their ability to reliably match salient phrases and rare entities in the query and to generalize to out-of-domain data. It has been argued that this is an inherent limitation of dense models. We rebut this claim by introducing the Salient Phrase Aware Retriever (SPAR), a dense retriever with the lexical matching capacity of a sparse model. We show that a dense Lexical Model Λ can be trained to imitate a sparse one, and SPAR is built by augmenting a standard dense retriever with Λ. Empirically, SPAR shows superior performance on a range of tasks including five question answering datasets, MS MARCO passage retrieval, as well as the EntityQuestions and BEIR benchmarks for out-of-domain evaluation, exceeding the performance of state-of-the-art dense and sparse retrievers. The code and models of SPAR are available at: https://github.com/facebookresearch/dpr-scale/tree/main/spar
Anthology ID:
2022.findings-emnlp.19
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
250–262
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.19
DOI:
Bibkey:
Cite (ACL):
Xilun Chen, Kushal Lakhotia, Barlas Oguz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta, and Wen-tau Yih. 2022. Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 250–262, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One? (Chen et al., Findings 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.findings-emnlp.19.pdf