Multi-View Document Representation Learning for Open-Domain Dense Retrieval
Shunyu Zhang, Yaobo Liang, Ming Gong, Daxin Jiang, Nan Duan · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
Dense retrieval has achieved impressive advances in first-stage retrieval from a largescale 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 multiview 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. * Work done during internship at Microsoft Research Asia.Q1: Where can people using iPods on planes view the device's interface?A1: Individual seat-back displays.Q2: What are two airlines that considered implementing iPod connections but did not join the 2007 agreement?A2: KLM and Air France.