A Survey on Legal Question Answering Systems

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.Comment: 57 pages, 1 figure, 10 table

Exploring the State of the Art in Legal QA Systems

Answering questions related to the legal domain is a complex task, primarily due to the intricate nature and diverse range of legal document systems. Providing an accurate answer to a legal query typically necessitates specialized knowledge in the relevant domain, which makes this task all the more challenging, even for human experts. QA (Question answering systems) are designed to generate answers to questions asked in human languages. They use natural language processing to understand questions and search through information to find relevant answers. QA has various practical applications, including customer service, education, research, and cross-lingual communication. However, they face challenges such as improving natural language understanding and handling complex and ambiguous questions. Answering questions related to the legal domain is a complex task, primarily due to the intricate nature and diverse range of legal document systems. Providing an accurate answer to a legal query typically necessitates specialized knowledge in the relevant domain, which makes this task all the more challenging, even for human experts. At this time, there is a lack of surveys that discuss legal question answering. To address this problem, we provide a comprehensive survey that reviews 14 benchmark datasets for question-answering in the legal field as well as presents a comprehensive review of the state-of-the-art Legal Question Answering deep learning models. We cover the different architectures and techniques used in these studies and the performance and limitations of these models. Moreover, we have established a public GitHub repository where we regularly upload the most recent articles, open data, and source code. The repository is available at: \url{https://github.com/abdoelsayed2016/Legal-Question-Answering-Review}

Beyond Logic Programming for Legal Reasoning

Logic programming has long being advocated for legal reasoning, and several approaches have been put forward relying upon explicit representation of the law in logic programming terms. In this position paper we focus on the PROLEG logic-programming-based framework for formalizing and reasoning with Japanese presupposed ultimate fact theory. Specifically, we examine challenges and opportunities in leveraging deep learning techniques for improving legal reasoning using PROLEG identifying four distinct options ranging from enhancing fact extraction using deep learning to end-to-end solutions for reasoning with textual legal descriptions. We assess advantages and limitations of each option, considering their technical feasibility, interpretability, and alignment with the needs of legal practitioners and decision-makers. We believe that our analysis can serve as a guideline for developers aiming to build effective decision-support systems for the legal domain, while fostering a deeper understanding of challenges and potential advancements by neuro-symbolic approaches in legal applications.Comment: Workshop on Logic Programming and Legal Reasoning, @ICLP 202

An outline of type-theoretical approaches to lexical semantics

We take the opportunity of the publication of some of the papers of the ESSLLI workshop  TYTLES (TYpes Theory and LExical Semantics, ESSLLI 2015, Barcelona) to provide an overview of the possibilities that type theory offer to model lexical semantics, especially the type theoretical frameworks that properly model compositional semantics

Rhetorically-based scalar-additivity: The view from Italian addirittura

Even-like particles have widely been analyzed as inducing scalar andadditive presuppositions (cf. Horn 1969; Karttunen & Peters 1979; Rooth 1992; Gast& van der Auwera 2011). However, the additivity of even has been controversialsince at least Rullmann 1997 and increasingly called into question (see Greenberg& Umbach 2021 for references); Greenberg specifically argues that scalar even-likeparticles can vary in additivity. This claim is surprising in light of the typologicalstudy in Gast & van der Auwera 2011, which subsumes even and similar expressionsunder a larger class of additive particles. Against this background, we present ananalysis of Italian addirittura, which with perfino has been described as scalaradditive(Visconti 2005) – but only optionally so – and is chosen preferentially overperfino precisely in those contexts that Greenberg takes to challenge the additivity ofeven. We argue, drawing on observations in Atayan 2017, that addirittura contrastswith perfino in deriving its scalar alternatives from rhetorical structure rather thanfocus structure. Once this is recognized we can view addirittura as additive, afterall, in a rhetorical sense we describe below