# Answer Retrieval in Legal Community Question Answering

arXiv: https://arxiv.org/abs/2401.04852 · HTML: https://mlsanity.com/paper/2401.04852/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2024.

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement](https://mlsanity.com/paper/2412.20468/) (2024)
- [From Answers to Guidance: A Proactive Dialogue System for Legal Documents](https://mlsanity.com/paper/2510.19723/) (2025)
- [LegalMALR:Multi-Agent Query Understanding and LLM-Based Reranking for Chinese Statute Retrieval](https://mlsanity.com/paper/2601.17692/) (2026)
- [Rhetorical-Role-Aware Retrieval-Augmented Generation for Legal Question Answering over Indian Supreme Court Judgments](https://mlsanity.com/paper/2608.06828/) (2026)

## Abstract (arXiv)

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.