Legal Question Answering System using Neural Attention

Morimoto, Ayaka; Kubo, Daiki; Sato, Motoki; Shindo, Haruo

doi:10.29007/4l2q

Cited by 11 publications

(8 citation statements)

References 3 publications

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“…focused on extracting contract elements based on the dataset provided by Chalkidis, Androutsopoulos, and Michos (2017). IR applications are the following: finding the related law articles for a given query (Kim, Xu, and Goebel 2017;Morimoto et al 2017;Nanda et al 2017;Do et al 2017), finding convenient relations and matching of cases and law provisions (Tang et al 2016), and extracting fact assertions in cases related to a query (Nejadgholi, Bougueng, and Witherspoon 2017). There are also studies on the legal domain-specific NER (Dozier et al 2010;Cardellino et al 2017;Luz de Araujo et al 2018;Sleimi et al 2018;Leitner, Rehm, and Moreno-Schneider 2019;Vardhan, Surana, and Tripathy 2020).…”

Section: Nlp In Lawmentioning

confidence: 99%

Gender bias in legal corpora and debiasing it

2022

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Word embeddings have become important building blocks that are used profoundly in natural language processing (NLP). Despite their several advantages, word embeddings can unintentionally accommodate some gender- and ethnicity-based biases that are present within the corpora they are trained on. Therefore, ethical concerns have been raised since word embeddings are extensively used in several high-level algorithms. Studying such biases and debiasing them have recently become an important research endeavor. Various studies have been conducted to measure the extent of bias that word embeddings capture and to eradicate them. Concurrently, as another subfield that has started to gain traction recently, the applications of NLP in the field of law have started to increase and develop rapidly. As law has a direct and utmost effect on people’s lives, the issues of bias for NLP applications in legal domain are certainly important. However, to the best of our knowledge, bias issues have not yet been studied in the context of legal corpora. In this article, we approach the gender bias problem from the scope of legal text processing domain. Word embedding models that are trained on corpora composed by legal documents and legislation from different countries have been utilized to measure and eliminate gender bias in legal documents. Several methods have been employed to reveal the degree of gender bias and observe its variations over countries. Moreover, a debiasing method has been used to neutralize unwanted bias. The preservation of semantic coherence of the debiased vector space has also been demonstrated by using high-level tasks. Finally, overall results and their implications have been discussed in the scope of NLP in legal domain.

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Section: Nlp In Lawmentioning

confidence: 99%

Gender bias in legal corpora and debiasing it

2022

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“…For instance, Kim et al (2015) propose a binary CNN-based classifier model for answering to the legal queries in the entailment phase. The entailment model introduced by Morimoto et al (2017) is instead based on MLP incorporating the attention mechanism. Nanda et al (2017) adopt a combination of partial string matching and topic clustering for the retrieval task, while they combine LSTM and CNN models for the entailment phase.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised law article mining based on deep pre-trained language representation models with application to the Italian civil code

Tagarelli

Simeri

2021

Artif Intell Law

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Modeling law search and retrieval as prediction problems has recently emerged as a predominant approach in law intelligence. Focusing on the law article retrieval task, we present a deep learning framework named LamBERTa, which is designed for civil-law codes, and specifically trained on the Italian civil code. To our knowledge, this is the first study proposing an advanced approach to law article prediction for the Italian legal system based on a BERT (Bidirectional Encoder Representations from Transformers) learning framework, which has recently attracted increased attention among deep learning approaches, showing outstanding effectiveness in several natural language processing and learning tasks. We define LamBERTa models by fine-tuning an Italian pre-trained BERT on the Italian civil code or its portions, for law article retrieval as a classification task. One key aspect of our LamBERTa framework is that we conceived it to address an extreme classification scenario, which is characterized by a high number of classes, the few-shot learning problem, and the lack of test query benchmarks for Italian legal prediction tasks. To solve such issues, we define different methods for the unsupervised labeling of the law articles, which can in principle be applied to any law article code system. We provide insights into the explainability and interpretability of our LamBERTa models, and we present an extensive experimental analysis over query sets of different type, for single-label as well as multi-label evaluation tasks. Empirical evidence has shown the effectiveness of LamBERTa, and also its superiority against widely used deep-learning text classifiers and a few-shot learner conceived for an attribute-aware prediction task.

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“…A few years after, the same authors proposed an improvement using deep siamese networks . Other authors decide to explore new kinds of systems using LSTM (John et al, 2016), the concept of neural attention (Morimoto et al, 2017). The concept of neural attention consists of freeing the encoder-decoders architecture from a fixed-length internal representation.…”

Section: Ontology-based Solutionsmentioning

confidence: 99%

A Survey on Legal Question Answering Systems

Martinez‐Gil¹

2021

Preprint

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Many legal professionals think that the explosion of information about local, regional, national, and international legislation makes their practice more costly, time-consuming, and even error-prone. The two main reasons for this are that most legislation is usually unstructured, and the tremendous amount and pace with which laws are released causes information overload in their daily tasks. In the case of the legal domain, the research community agrees that a system allowing to generate automatic responses to legal questions could substantially impact many practical implications in daily activities.The degree of usefulness is such that even a semi-automatic solution could significantly help to reduce the workload to be faced. This is mainly because a Question Answering system could be able to automatically process a massive amount of legal resources to answer a question or doubt in seconds, which means that it could save resources in the form of effort, money, and time to many professionals in the legal sector. In this work, we quantitatively and qualitatively survey the solutions that currently exist to meet this challenge.

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Legal Question Answering System using Neural Attention

Cited by 11 publications

References 3 publications

Gender bias in legal corpora and debiasing it

Gender bias in legal corpora and debiasing it

Unsupervised law article mining based on deep pre-trained language representation models with application to the Italian civil code

A Survey on Legal Question Answering Systems

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