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Recognizing entailments in legal texts using sentence encoding-based and decomposable attention models

12 pagesPublished: June 3, 2017

Abstract

This paper presents an end-to-end question answering system for legal texts. This system includes two main phases. In the first phase, our system will retrieve articles from Japanese Civil Code that are relevant with the given question using the cosine distance after the given question and articles are converted into vectors using TF-IDF weighting scheme. Then, a ranking model can be applied to re-rank these retrieved articles using a learning to rank algorithm and annotated corpus. In the second phase, we adapted two deep learning models, which has been proposed for the Natural language inference task, to check the entailment relationship between a question and its related articles including a sentence encoding-based model and a decomposable attention model. Experimental results show that our approaches can be a promising approach for information extraction/entailment in legal texts.

Keyphrases: deep learning, entailment extraction, Information Retrieval, Legal Question Answering

In: Ken Satoh, Mi-Young Kim, Yoshinobu Kano, Randy Goebel and Tiago Oliveira (editors). COLIEE 2017. 4th Competition on Legal Information Extraction and Entailment, vol 47, pages 31--42

Links:
BibTeX entry
@inproceedings{COLIEE2017:Recognizing_entailments_in_legal,
  author    = {Nguyen Truong Son and Phan Viet Anh and Nguyen Le Minh},
  title     = {Recognizing entailments in legal texts using sentence encoding-based and decomposable attention models},
  booktitle = {COLIEE 2017. 4th Competition on Legal Information Extraction and Entailment},
  editor    = {Ken Satoh and Mi-Young Kim and Yoshinobu Kano and Randy Goebel and Tiago Oliveira},
  series    = {EPiC Series in Computing},
  volume    = {47},
  pages     = {31--42},
  year      = {2017},
  publisher = {EasyChair},
  bibsource = {EasyChair, http://www.easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/wHFc},
  doi       = {10.29007/gn47}}
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