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Various Legal Factors Extraction Based on Machine Reading Comprehension

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Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 13026))

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Abstract

With the rapid growth of legal cases, professionals are under pressure to go through lengthy documents and grasp informative pieces of text in the limited time. Most of the existing techniques focus on simple legal information retrieval task, such as name or address of the prosecutor or the defendant, which can be easily accomplished with the help of handcrafted patterns or sequence labeling methods. Yet complicated texts always challenge such pattern-based methods and sequence labeling approaches. These texts state the same facts or describe the same events, but they do not share common or similar patterns. In this paper, we design a unified framework to extract legal information in various formats, including directly extracted information (a piece of span) and information that needs to be deduced. The framework follows the methodology to answer questions in machine reading comprehension (MRC) tasks. We treat the extraction fact labels as the counterpart of questions in MRC task and propose several strategies to represent them. We construct several datasets regarding different cases for training and testing. Our best strategy achieves up to 4% enhancement in F1 score on each dataset compared to the MRC baseline.

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Acknowledgments

The authors would like to thank all the reviewers for their insight reviews. This paper is funded by National Key R&D Program of China (No.2018YFC0807701).

Author information

Authors and Affiliations

  1. State Key Laboratory of Cognitive Intelligence, iFLYTEK Research, Beijing, China

    Beichen Wang, Ziyue Wang, Baoxin Wang, Dayong Wu, Zhigang Chen, Shijin Wang & Guoping Hu

  2. iFLYTEK AI Research (Hebei), Langfang, China

    Shijin Wang

Authors
  1. Beichen Wang

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  2. Ziyue Wang

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  3. Baoxin Wang

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  4. Dayong Wu

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  5. Zhigang Chen

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  6. Shijin Wang

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  7. Guoping Hu

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Corresponding author

Correspondence toZiyue Wang.

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Hongfei Lin

  2. Department of Computer Science, Tsinghua University, Beijing, China

    Min Zhang

  3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Liang Pang

Appendix

Appendix

Table 5. Data augmentation of other two datasets. We selected two representative questions from each dataset. We do not translate sentences after data augmentation (DA) because DA is usually a paraphrasing of the original questions, and the English version could remain unchanged.
Table 6. Results of general questions, reported inExactMatch(%),F1 score(%),Precision(%) andRecall(%). The meaning of each experiment in this table is the same as that in Table 4. It is noted that the extraction results of the questions whose answer is no in the datasets of recourse for labor remuneration and refusal to execute judgments or rulings are poor, and even the results of many experiments are 0. As shown in Table 2, the questions with negative answer are originally very rare, and we do not build the datasets specifically for this situation, which made it difficult to extract, or simply do not show in the test sets.

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Wang, B.et al. (2021). Various Legal Factors Extraction Based on Machine Reading Comprehension. In: Lin, H., Zhang, M., Pang, L. (eds) Information Retrieval. CCIR 2021. Lecture Notes in Computer Science(), vol 13026. Springer, Cham. https://doi.org/10.1007/978-3-030-88189-4_2

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