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Artificial Intelligence and Law

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07.07.2021 | Original Research

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

verfasst von: Sinh Trong Vu, Minh Le Nguyen, Ken Satoh

Erschienen in: Artificial Intelligence and Law | Ausgabe 2/2022

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

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Titel: Abstract meaning representation for legal documents: an empirical research on a human-annotated dataset
verfasst von: Sinh Trong Vu
Minh Le Nguyen
Ken Satoh
Publikationsdatum: 07.07.2021
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence and Law / Ausgabe 2/2022
Print ISSN: 0924-8463
Elektronische ISSN: 1572-8382
DOI: https://doi.org/10.1007/s10506-021-09292-6

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