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    <abstract>Dense retrieval has achieved impressive advances in first-stage retrieval from a large-scale document collection, which is built on bi-encoder architecture to produce single vector representation of query and document. However, a document can usually answer multiple potential queries from different views. So the single vector representation of a document is hard to match with multi-view queries, and faces a semantic mismatch problem. This paper proposes a multi-view document representation learning framework, aiming to produce multi-view embeddings to represent documents and enforce them to align with different queries. First, we propose a simple yet effective method of generating multiple embeddings through viewers. Second, to prevent multi-view embeddings from collapsing to the same one, we further propose a global-local loss with annealed temperature to encourage the multiple viewers to better align with different potential queries. Experiments show our method outperforms recent works and achieves state-of-the-art results.</abstract>
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%0 Conference Proceedings
%T Multi-View Document Representation Learning for Open-Domain Dense Retrieval
%A Zhang, Shunyu
%A Liang, Yaobo
%A Gong, Ming
%A Jiang, Daxin
%A Duan, Nan
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zhang-etal-2022-multi
%X Dense retrieval has achieved impressive advances in first-stage retrieval from a large-scale document collection, which is built on bi-encoder architecture to produce single vector representation of query and document. However, a document can usually answer multiple potential queries from different views. So the single vector representation of a document is hard to match with multi-view queries, and faces a semantic mismatch problem. This paper proposes a multi-view document representation learning framework, aiming to produce multi-view embeddings to represent documents and enforce them to align with different queries. First, we propose a simple yet effective method of generating multiple embeddings through viewers. Second, to prevent multi-view embeddings from collapsing to the same one, we further propose a global-local loss with annealed temperature to encourage the multiple viewers to better align with different potential queries. Experiments show our method outperforms recent works and achieves state-of-the-art results.
%R 10.18653/v1/2022.acl-long.414
%U https://aclanthology.org/2022.acl-long.414
%U https://doi.org/10.18653/v1/2022.acl-long.414
%P 5990-6000

Markdown (Informal)

[Multi-View Document Representation Learning for Open-Domain Dense Retrieval](https://aclanthology.org/2022.acl-long.414) (Zhang et al., ACL 2022)

• Multi-View Document Representation Learning for Open-Domain Dense Retrieval (Zhang et al., ACL 2022)

ACL

• Shunyu Zhang, Yaobo Liang, Ming Gong, Daxin Jiang, and Nan Duan. 2022. Multi-View Document Representation Learning for Open-Domain Dense Retrieval. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5990–6000, Dublin, Ireland. Association for Computational Linguistics.