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2019

Understanding the Behaviors of BERT in Ranking

Qiao, Yifan, Xiong, Chenyan, Liu, Zhenghao et al.

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This paper studies the performances and behaviors of BERT in ranking tasks.

We explore several different ways to leverage the pre-trained BERT and fine-tune it on two ranking tasks: MS MARCO passage reranking and TREC Web Track ad hoc document ranking.
Experimental results on MS MARCO demonstrate the strong effectiveness of BERT in question-answering focused passage ranking tasks, as well as the fact that BERT is a strong interaction-based seq2seq matching model.
Experimental results on TREC show the gaps between the BERT pre-trained on surrounding contexts and the needs of ad hoc document ranking.

Understanding the Behaviors of BERT in Ranking · Around

Built on

A deep relevance matching model for ad-hoc retrieval. In Proceedings of CIKM 2016
Jiafeng Guo, Yixing Fan, Qingyao Ai, and W Bruce Croft. 2016 · 2016
Earlier work this paper cites.
Deeprank: A new deep architecture for relevance ranking in information retrieval. In Proceedings of CIKM 2017
Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Jingfang Xu, and Xueqi Cheng. 2017 · 2017
Earlier work this paper cites.
Attention is all you need. In Proceedings of NeuIPS 2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.

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Then

Bert: Pre-training of deep bidirectional transformers for language understanding
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Later among the works it cites.
Passage Re-ranking with BERT
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Closest in time.

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