Leveraging Passage-level Cumulative Gain for Document Ranking

Zhijing Wu, Jiaxin Mao, Yiqun Liu, Jingtao Zhan, Yukun Zheng, Min Zhang, Shaoping Ma

科研成果: 书/报告/会议事项章节会议稿件同行评审

37 引用 (Scopus)

摘要

Document ranking is one of the most studied but challenging problems in information retrieval (IR) research. A number of existing document ranking models capture relevance signals at the whole document level. Recently, more and more research has begun to address this problem from fine-grained document modeling. Several works leveraged fine-grained passage-level relevance signals in ranking models. However, most of these works focus on context-independent passage-level relevance signals and ignore the context information, which may lead to inaccurate estimation of passage-level relevance. In this paper, we investigate how information gain accumulates with passages when users sequentially read a document. We propose the context-aware Passage-level Cumulative Gain (PCG), which aggregates relevance scores of passages and avoids the need to formally split a document into independent passages. Next, we incorporate the patterns of PCG into a BERT-based sequential model called Passage-level Cumulative Gain Model (PCGM) to predict the PCG sequence. Finally, we apply PCGM to the document ranking task. Experimental results on two public ad hoc retrieval benchmark datasets show that PCGM outperforms most existing ranking models and also indicates the effectiveness of PCG signals. We believe that this work contributes to improving ranking performance and providing more explainability for document ranking.

源语言英语
主期刊名The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
出版商Association for Computing Machinery, Inc
2421-2431
页数11
ISBN(电子版)9781450370233
DOI
出版状态已出版 - 20 4月 2020
已对外发布
活动29th International World Wide Web Conference, WWW 2020 - Taipei, 中国台湾
期限: 20 4月 202024 4月 2020

出版系列

姓名The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020

会议

会议29th International World Wide Web Conference, WWW 2020
国家/地区中国台湾
Taipei
时期20/04/2024/04/20

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引用此

Wu, Z., Mao, J., Liu, Y., Zhan, J., Zheng, Y., Zhang, M., & Ma, S. (2020). Leveraging Passage-level Cumulative Gain for Document Ranking. 在 The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020 (页码 2421-2431). (The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020). Association for Computing Machinery, Inc. https://doi.org/10.1145/3366423.3380305