We present an annotation scheme describing the argument structure of judgement documents, a central construct in Japanese law. To support the final goal of this work, namely summarisation aimed at the legal professions, we have designed blueprint models of summaries of various granularities, and our annotation model in turn is fitted around the information needed for the summaries. In this paper we report results of a manual annotation study, showing that the annotation is stable. The annotated corpus we created contains (...) 89 documents. We also designed and implemented the first two stages of an algorithm for the automatic extraction of argument structure, and present evaluation results. (shrink)

How can we assess the epistemic quality of democratic decision-making? Sceptics doubt such assessments are possible, as they must rely on controversial substantive standards of truth and rightness. Challenging that scepticism, this paper suggests a procedure-independent standard for assessing the epistemic quality of democratic decision-making by evaluating whether it is adequately supported by reasons. Adequate support for conclusion is a necessary aspect of epistemic quality for any epistemic justification of democracy, though particularly relevant to theories that emphasize public deliberation. Finding (...) existing methods for measuring the quality of public discourse to fall short, we draw on an epistemological theory of argumentation to provide a more sophisticated approach to evaluating the epistemic quality of democratic decision-making, illustrate how the approach can be used, and demonstrate its relevance for the epistemic turn in democratic theory. (shrink)

In spite of significant research efforts, argument technologies do not seem poised to scale up as much as most commentators would hope or even predict. In this paper, I discuss what obstacles bar the way to more widespread success of argument technologies and venture some suggestions on how to circumvent such difficulties: doing so will require a significant shift in how this research area is typically understood and practiced. I begin by exploring a much broader yet closely related question: To (...) what extent are people natively good at arguing? This issue has always been central to philosophical reflection and it has become even more urgent nowadays, with the explosion of persuasive technologies and unprecedented opportunities for large-scale social influence. The answer hinges on what aspect of argumentation is taken under consideration: evidence suggests that people are relatively bad at analyzing the structure of arguments, especially when these are presented out of context and in abstract terms; in contrast, data show that even laymen tend to excel in the interactive practice of argumentation, in particular when motivation is high and something significant is at stake. Unfortunately, current argument technologies are more closely tailored to the former type of activity than to the latter, which is the main reason behind their relative lack of success with the general public. Changing this state of affair will require a commitment to ecological argument technologies: that is, technologies designed to support real-time, engaging and meaningful argumentative interactions performed by laypeople in their ordinary life, instead of catering to the highly specific needs of a minority of niche users. (shrink)

This volume presents a selection of papers reflecting key theoretical issues in argumentation theory. Its six sections are devoted to specific themes, including the analysis and evaluation of argumentation, argument schemes and the contextual embedding of argumentation. The section on general perspectives on argumentation discusses the trends of empiricalization, contextualization and formalization, offers descriptions of the analytical and evaluative tools of informal logic, and highlights selected principles that argumentation theorists do and do not agree upon. In turn, the section on (...) linguistic approaches to argumentation focuses on the problem of distinguishing between explanation and argument, while also elaborating on the role of verbal indicators of argument schemes. All essays included in this volume point out notable recent developments in the study of argumentation. (shrink)

The representation and classification of the structure of natural arguments has been one of the most important aspects of Aristotelian and medieval dialectical and rhetorical theories. This traditional approach is represented nowadays in models of argumentation schemes. The purpose of this article is to show how arguments are characterized by a complex combination of two levels of abstraction, namely, semantic relations and types of reasoning, and to provide an effective and comprehensive classification system for this matrix of semantic and quasilogical (...) connections. To this purpose, we propose a dichotomous criterion of classification, transcending both levels of abstraction and representing not what an argument is but how it is understood and interpreted. The schemes are grouped according to an end-means criterion, which is strictly bound to the ontological structure of the conclusion and the premises. On this view, a scheme can be selected according to the intended or reconstructed purpose of an argument and the possible strategies that can be used to achieve it. (shrink)

We provide a retrospective of 25 years of the International Conference on AI and Law, which was first held in 1987. Fifty papers have been selected from the thirteen conferences and each of them is described in a short subsection individually written by one of the 24 authors. These subsections attempt to place the paper discussed in the context of the development of AI and Law, while often offering some personal reactions and reflections. As a whole, the subsections build into (...) a history of the last quarter century of the field, and provide some insights into where it has come from, where it is now, and where it might go. (shrink)

Proportionality is a central and globally spread argumentation technique in public law. This article provides a conceptual introduction to proportionality and argues that such a domain-specific form of argumentation is particularly interesting for argument mining. As a major contribution of this article, we share a new dataset for which proportionality has been annotated. The dataset consists of 300 German Federal Constitutional Court decisions annotated at the sentence level (54,929 sentences). In addition to separating textual parts, a fine-grained system of proportionality (...) categories was used. Finally, we used these data for a classification task. We built classifiers that predict whether or not proportionality is invoked in a sentence. We employed several models, including neural and deep learning models and transformers. A BERT-BiLSTM-CRF model performed best. (shrink)

This paper explains the importance of classifying argumentation schemes, and outlines how schemes are being used in current research in artificial intelligence and computational linguistics on argument mining. It provides a survey of the literature on scheme classification. What are so far generally taken to represent a set of the most widely useful defeasible argumentation schemes are surveyed and explained systematically, including some that are difficult to classify. A new classification system covering these centrally important schemes is built.

One of the crucial problems of argumentation schemes as illustrated in (Walton, Reed & Macagno 2008) is their practical use for the purpose of analyzing texts and producing arguments. The high number and the lack of a classification criterion make this instrument extremely difficult to apply practically. The purpose of this paper is to analyze the structure of argumentation schemes and outline a possible criterion of classification based on alternative and mutually-exclusive possibilities. Such a criterion is based not on what (...) an argument is, but how it can be understood and interpreted. The schemes are grouped according to an end-means principle, which is strictly bound to the ontological structure of the conclusion and the premises. On this view, a scheme can be selected according to the intended or reconstructed purpose of an argument and the possible strategies that can be used to achieve it. (shrink)

Information technology is so ubiquitous and AI’s progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications (whether in the law or elsewhere) help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should be (...) good for us. But how to establish proper safeguards for AI? One strong answer readily available is: consider the problems and solutions studied in AI & Law. AI & Law has worked on the design of social, explainable, responsible AI aligned with human values for decades already, AI & Law addresses the hardest problems across the breadth of AI (in reasoning, knowledge, learning and language), and AI & Law inspires new solutions (argumentation, schemes and norms, rules and cases, interpretation). It is argued that the study of AI as Law supports the development of an AI that is good for us, making AI & Law more relevant than ever. (shrink)

This paper outlines an integrated approach to trust and relevance with respect to arguments: in particular, it is suggested that trust in relevance has a central role in argumentation. We first distinguish two types of argumentative relevance: internal relevance, i.e. the extent to which a premise has a bearing on its purported conclusion, and external relevance, i.e. a measure of how much a whole argument is pertinent to the matter under discussion, in the broader dialogical context where it is proposed. (...) Then, we argue that judgements of internal relevance heavily rely on trust, and that such trust, although occasionally misplaced , is nonetheless based on several reasons, and thus often justified, by either epistemic or pragmatic considerations. We conclude by sketching potential methods to formally model trust in argumentative relevance, and briefly discussing the technological implications of this line of research. (shrink)

Argumentation schemes are forms of reasoning that are fallible but correctable within a self-correcting framework. Their use provides a basis for taking rational action or for reasonably accepting a conclusion as a tentative hypothesis, but they are not deductively valid. We argue that teleological reasoning can provide the basis for justifying the use of argument schemes both in monological and dialogical reasoning. We consider how such a teleological justification, besides being inspired by the aim of directing a bounded cognizer to (...) true belief and correct choices, needs to take into account the attitudes of dialogue partners as well as normative models of dialogue and communicative activity types, in particular social and cultural settings. (shrink)

One of the main challenges for computational legal research is drawing up innovative heuristics to derive actionable knowledge from legal documents. While a large part of the research has been so far devoted to the extraction of purely legal information, less attention has been paid to seeking out in the texts the clues of more complex entities: legally relevant facts whose detection requires to link and interpret, as a unified whole, legal information and results of empirical analyses. This paper presents (...) an ongoing research that points in this direction, trying to devise new ways to support public prosecutors in assessing the dangerousness of individuals and groups under investigation, an activity that precisely relies on the cross-sectional evaluation of legal and empirical data. A knowledge mining strategy will be outlined that lines up, into a single metaheuristic model, information extraction, network-based inference, machine learning and visual analytics. We will focus, in particular, on the integration of graph-based inference and machine learning methods used both to support classification tasks and to explore new forms of man-machine cooperation. Experiments made involving public prosecutors from the Italian Anti-Mafia Investigation Directorate and using data from real investigations have not only shown the potentialities of our approach but also offered an opportunity to reflect on the role we could assign to AI when thinking about the future of legal science and practice. (shrink)

This paper compares the features and methods of the two leading implemented systems that offer a tool for helping a user to find or invent arguments to support or attack a designated conclusion, the Carneades Argumentation System and the IBM Watson Debater tool. The central aim is to contribute to the understanding of scholars in informal logic, rhetoric and argumentation on how these two software systems can be useful for them. One contribution of the paper is to explain to these (...) potential users how the two tools are applicable to the task of inventing arguments by using some simple illustrative examples. Another is to redefine the structure of argument invention as a procedure. (shrink)

Identifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field. However, there has been a major discrepancy between the way natural language processing (NLP) researchers model and annotate arguments in court decisions and the way legal experts understand and analyze legal argumentation. While computational approaches typically simplify arguments into generic premises and claims, arguments in legal research usually exhibit a rich typology that is important for gaining insights (...) into the particular case and applications of law in general. We address this problem and make several substantial contributions to move the field forward. First, we design a new annotation scheme for legal arguments in proceedings of the European Court of Human Rights (ECHR) that is deeply rooted in the theory and practice of legal argumentation research. Second, we compile and annotate a large corpus of 373 court decisions (2.3M tokens and 15k annotated argument spans). Finally, we train an argument mining model that outperforms state-of-the-art models in the legal NLP domain and provide a thorough expert-based evaluation. All datasets and source codes are available under open lincenses athttps://github.com/trusthlt/mining-legal-arguments. (shrink)

The concept of ‘relevance’ is crucial to legal information retrieval, but because of its intuitive understanding it goes undefined too easily and unexplored too often. We discuss a conceptual framework on relevance within legal information retrieval, based on a typology of relevance dimensions used within general information retrieval science, but tailored to the specific features of legal information. This framework can be used for the development and improvement of legal information retrieval systems.

The Argument Web is maturing as both a platform built upon a synthesis of many contemporary theories of argumentation in philosophy and also as an ecosystem in which various applications and application components are contributed by different research groups around the world. It already hosts the largest publicly accessible corpora of argumentation and has the largest number of interoperable and cross compatible tools for the analysis, navigation and evaluation of arguments across a broad range of domains, languages and activity types. (...) Such interoperability is key in allowing innovative combinations of tool and data reuse that can further catalyse the development of the field of computational argumentation. The aim of this paper is to summarise the key foundations, the recent advances and the goals of the Argument Web, with a particular focus on demonstrating the relevance to, and roots in, philosophical argumentation theory. (shrink)

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal’s second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely (...) argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments. (shrink)

Parsing of argumentative structures has become a very active line of research in recent years. Like discourse parsing or any other natural language task that requires prediction of linguistic struc...

Argumentation mining is an advanced form of human language understanding by the machine. This is a challenging task for a machine. When sufficient explicit discourse markers are present in the lang...

The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This special issue marks the 30th anniversary of the journal by reviewing the progress of the field through thirty commentaries on landmark papers and groups of papers from that journal.

In this work we study, design, and evaluate computational methods to support interpretation of statutory terms. We propose a novel task of discovering sentences for argumentation about the meaning of statutory terms. The task models the analysis of past treatment of statutory terms, an exercise lawyers routinely perform using a combination of manual and computational approaches. We treat the discovery of sentences as a special case of ad hoc document retrieval. The specifics include retrieval of short texts, specialized document types, (...) and, above all, the unique definition of document relevance provided in detailed annotation guidelines. To support our experiments we assembled a data set comprising 42 queries which we plan to release to the public in the near future in order to support further research. Most importantly, we investigate the feasibility of developing a system that responds to a query with a list of sentences that mention the term in a way that is useful for understanding and elaborating its meaning. This is accomplished by a systematic assessment of different features that model the sentences’ usefulness for interpretation. We combine features into a compound measure that accounts for multiple aspects. The definition of the task, the assembly of the data set, and the detailed task analysis provide a solid foundation for employing a learning-to-rank approach. (shrink)

Logic-based approaches to legal problem solving model the rule-governed nature of legal argumentation, justification, and other legal discourse but suffer from two key obstacles: the absence of efficient, scalable techniques for creating authoritative representations of legal texts as logical expressions; and the difficulty of evaluating legal terms and concepts in terms of the language of ordinary discourse. Data-centric techniques can be used to finesse the challenges of formalizing legal rules and matching legal predicates with the language of ordinary parlance by (...) exploiting knowledge latent in legal corpora. However, these techniques typically are opaque and unable to support the rule-governed discourse needed for persuasive argumentation and justification. This paper distinguishes representative legal tasks to which each approach appears to be particularly well suited and proposes a hybrid model that exploits the complementarity of each. (shrink)

The dominant approach to argument mining has been to treat it as a machine learning problem based upon superficial text features, and to treat the relationships between arguments as either support...