@inproceedings{zhang2022multi-view,
author = {Zhang, Shunyu and Liang, Yaobo and Gong (YIMING), Ming and Jiang (姜大昕), Daxin and Duan, Nan},
title = {Multi-View Document Representation Learning for Open-Domain Dense Retrieval},
booktitle = {ACL 2022},
year = {2022},
month = {May},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/multi-view-document-representation-learning-for-open-domain-dense-retrieval/},
}