Long Legal Article Question Answering via Cascaded Key Segment Learning (Student Abstract)


  • Shugui Xie Wuhan University of Technology
  • Lin Li Wuhan University of Technology
  • Jingling Yuan Wuhan University of Technology
  • Qing Xie Wuhan University of Technology
  • Xiaohui Tao University of Southern Queensland




Legal Question Answering, Machine Reading Comprehension, Key Segment


Current sentence-level evidence extraction based methods may lose the discourse coherence of legal articles since they tend to make the extracted sentences scattered over the article. To solve the problem, this paper proposes a Cascaded Answer-guided key segment learning framework for long Legal article Question Answering, namely CALQA. The framework consists of three cascaded modules: Sifter, Reader, and Responder. The Sifter transfers a long legal article into several segments and works in an answer-guided way by automatically sifting out key fact segments in a coarse-to-fine approach through multiple iterations. The Reader utilizes a set of attention mechanisms to obtain semantic representations of the question and key fact segments. Finally, considering it a multi-label classification task the Responder predicts final answers in a cascaded manner. CALQA outperforms state-of-the-art methods in CAIL 2021 Law dataset.




How to Cite

Xie, S., Li, L., Yuan, J., Xie, Q., & Tao, X. (2023). Long Legal Article Question Answering via Cascaded Key Segment Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16364-16365. https://doi.org/10.1609/aaai.v37i13.27042