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Answer Retrieval in Legal Community Question Answering

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Abstract

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 CEFS, a novel cross-encoder (CE) re-ranker based on thefine-grainedstructured inputs. CEFS uses additional structured information in the CQA data to improve the effectiveness of cross-encoder re-rankers. Furthermore, we proposeLegalQA: 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.

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Notes

  1. 1.

    We interchangeably use the word answer and response to refer to the content written by the professional lawyer.

  2. 2.
  3. 3.

    We refer to the person who posts a question as a user or questioner throughout this paper.

  4. 4.
  5. 5.
  6. 6.

    We use the implementation available athttps://github.com/seatgeek/thefuzz.

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Acknowledgments

This work was supported by the EU Horizon 2020 ITN/ETN on Domain Specific Systems for Information Extraction and Retrieval (H2020-EU.1.3.1., ID: 860721).

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Authors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Arian Askari, Zihui Yang, Zhaochun Ren & Suzan Verberne

Authors
  1. Arian Askari

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  2. Zihui Yang

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  3. Zhaochun Ren

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  4. Suzan Verberne

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Corresponding author

Correspondence toArian Askari.

Editor information

Editors and Affiliations

  1. Georgetown University, Washington, WA, USA

    Nazli Goharian

  2. University of Pisa, PISA, Pisa, Italy

    Nicola Tonellotto

  3. King's College London, London, UK

    Yulan He

  4. University College London, London, UK

    Aldo Lipani

  5. University of Glasgow, Glasgow, UK

    Graham McDonald

  6. University of Glasgow, Glasgow, UK

    Craig Macdonald

  7. University of Glasgow, Glasgow, UK

    Iadh Ounis

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Askari, A., Yang, Z., Ren, Z., Verberne, S. (2024). Answer Retrieval in Legal Community Question Answering. In: Goharian, N.,et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14610. Springer, Cham. https://doi.org/10.1007/978-3-031-56063-7_40

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