Understanding the Behaviors of BERT in Ranking
Abstract
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. Analyses illustrate how BERT allocates its attentions between query-document tokens in its Transformer layers, how it prefers semantic matches between paraphrase tokens, and how that differs with the soft match patterns learned by a click-trained neural ranker.
Keywords
Cite
@article{arxiv.1904.07531,
title = {Understanding the Behaviors of BERT in Ranking},
author = {Yifan Qiao and Chenyan Xiong and Zhenghao Liu and Zhiyuan Liu},
journal= {arXiv preprint arXiv:1904.07531},
year = {2019}
}
Comments
There is an error in Table 1 and we will update them to correct results. Please refer to MS MARCO Leaderboard for the actually evaluation results