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Abstract meaning representation for legal documents: an empirical research on a human-annotated dataset

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Abstract

Natural language processing techniques contribute more and more in analyzing legal documents recently, which supports the implementation of laws and rules using computers. Previous approaches in representing a legal sentence often based on logical patterns that illustrate the relations between concepts in the sentence, often consist of multiple words. Those representations cause the lack of semantic information at the word level. In our work, we aim to tackle such shortcomings by representing legal texts in the form of abstract meaning representation (AMR), a graph-based semantic representation that gains lots of polarity in NLP community recently. We present our study in AMR Parsing (producing AMR from natural language) and AMR-to-text Generation (producing natural language from AMR) specifically for legal domain. We also introduce JCivilCode, a human-annotated legal AMR dataset which was created and verified by a group of linguistic and legal experts. We conduct an empirical evaluation of various approaches in parsing and generating AMR on our own dataset and show the current challenges. Based on our observation, we propose our domain adaptation method applying in the training phase and decoding phase of a neural AMR-to-text generation model. Our method improves the quality of text generated from AMR graph compared to the baseline model. (This work is extended from our two previous papers: “An Empirical Evaluation of AMR Parsing for Legal Documents”, published in the Twelfth International Workshop on Juris-informatics (JURISIN) 2018; and “Legal Text Generation from Abstract Meaning Representation”, published in the 32nd International Conference on Legal Knowledge and Information Systems (JURIX) 2019.).

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Notes

  1. We keep the original trained models without retrained on the new dataset LDC2017T10

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Acknowledgments

This work was supported by JST CREST Grant Number JPMJCR1513 Japan, JSPS Kakenhi Grant Number 20H04295, 20K2046, and 20K20625. The research also was supported in part by the Asian Office of Aerospace R\&D (AOARD), Air Force Office of Scientific Research (Grant no. FA2386-19-1-4041).

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  1. Japan Advanced Institute of Science and Technology, Ishikawa, 923-1292, Japan

    Sinh Trong Vu & Minh Le Nguyen

  2. National Institute of Informatics, Tokyo, 100-0003, Japan

    Ken Satoh

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  1. Sinh Trong Vu
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  2. Minh Le Nguyen
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Correspondence to Sinh Trong Vu or Minh Le Nguyen.

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Vu, S.T., Le Nguyen, M. & Satoh, K. Abstract meaning representation for legal documents: an empirical research on a human-annotated dataset. Artif Intell Law 30, 221–243 (2022). https://doi.org/10.1007/s10506-021-09292-6

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