Name
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. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Information Retrieval | Journal |
Volume | Citations | PageRank |
abs/1904.07531 | 3 | 0.38 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yifan Qiao | 1 | 6 | 3.50 |
| Chen-Yan Xiong | 2 | 405 | 30.82 |
| Zheng-Hao Liu | 3 | 9 | 3.20 |
| Zhiyuan Liu | 4 | 2037 | 123.68 |