  The task of answer retrieval in the legal domain aims to help users to seek
relevant legal advice from massive amounts of professional responses. Two main
challenges hinder applying existing answer retrieval approaches in other
domains to the legal domain: (1) a huge knowledge gap between lawyers and
non-professionals; and (2) a mix of informal and formal content on legal QA
websites. To tackle these challenges, we propose CE_FS, a novel cross-encoder
(CE) re-ranker based on the fine-grained structured inputs. CE_FS uses
additional structured information in the CQA data to improve the effectiveness
of cross-encoder re-rankers. Furthermore, we propose LegalQA: a real-world
benchmark dataset for evaluating answer retrieval in the legal domain.
Experiments conducted on LegalQA show that our proposed method significantly
outperforms strong cross-encoder re-rankers fine-tuned on MS MARCO. Our novel
finding is that adding the question tags of each question besides the question
description and title into the input of cross-encoder re-rankers structurally
boosts the rankers' effectiveness. While we study our proposed method in the
legal domain, we believe that our method can be applied in similar applications
in other domains.

Information Retrieval