Multi-View Document Representation Learning for Open-Domain Dense Retrieval

• URL: http://arxiv.org/abs/2203.08372v1
• Date: Wed, 16 Mar 2022 03:36:38 GMT
• Title: Multi-View Document Representation Learning for Open-Domain Dense Retrieval
• Authors: Shunyu Zhang, Yaobo Liang, Ming Gong, Daxin Jiang, Nan Duan
• Abstract summary: This paper proposes a multi-view document representation learning framework. It aims to produce multi-view embeddings to represent documents and enforce them to align with different queries. Experiments show our method outperforms recent works and achieves state-of-the-art results.
• Score: 87.11836738011007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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