Argumentation is the process by which humans rationally elaborate their thoughts and opinions in written (e.g., essays) or spoken (e.g., debates) contexts. Argument Mining research, however, has been focused on either written argumentation or spoken argumentation but without considering any additional information, e.g., speech acts and intentions. In this paper, we present an overview of DialAM-2024, the first shared task in dialogical argument mining, where argumentative relations and speech illocutions are modelled together in a unified framework. The task was divided into two different sub-tasks: the identification of propositional relations and the identification of illocutionary relations. Six different teams explored different methodologies to leverage both sources of information to reconstruct argument maps containing the locutions uttered in the speeches and the argumentative propositions implicit in them. The best performing team achieved an F1-score of 67.05% in the overall evaluation of the reconstruction of complete argument maps, considering both sub-tasks included in the DialAM-2024 shared task.

It is known from large-scale crowd experimentation that some people are innately better at analysing complex situations and making justified predictions – the so-called ‘superforecasters’. Surprisingly, however, there has to date been no work exploring the role played by the reasoning in those justifications. Bag-of-words analyses might tell us something, but the real value lies in understanding what features of reasoning and argumentation lead to better forecasts – both in providing an objective measure for argument quality, and even more importantly, in providing guidance on how to improve forecasting performance. The work presented here covers the creation of a unique dataset of such prediction rationales, the structure of which naturally lends itself to partially automated annotation which in turn is used as the basis for subsequent manual enhancement that provides a uniquely fine-grained and close characterisation of the structure of argumentation, with potential impact on forecasting domains from intelligence analysis to investment decision-making.

Previous work on the automatic identification of fallacies in natural language text has typically approached the problem in constrained experimental setups that make it difficult to understand the applicability and usefulness of the proposals in the real world. In this paper, we present the first analysis of the limitations that these data-driven approaches could show in real situations. For that purpose, we first create a validation corpus consisting of natural language argumentation schemes. Second, we provide new empirical results to the emerging task of identifying fallacies in natural language text. Third, we analyse the errors observed outside of the testing data domains considering the new validation corpus. Finally, we point out some important limitations observed in our analysis that should be taken into account in future research in this topic. Specifically, if we want to deploy these systems in the Wild.

Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general.

Understanding the inferential principles underpinning an argument is essential to the proper interpretation and evaluation of persuasive discourse. Argument schemes capture the conventional patterns of reasoning appealed to in persuasion. The empirical study of these patterns relies on the availability of data about the actual use of argumentation in communicative practice. Annotated corpora of argument schemes, however, are scarce, small, and unrepresentative. Aiming to address this issue, we present one step in the development of improved datasets by integrating the Argument Scheme Key – a novel annotation method based on one of the most popular typologies of argument schemes – into the widely used OVA software for argument analysis.

This paper presents a method of extracting argumentative structure from natural language text. The approach presented is based on the way in which we understand an argument being made, not just from the words said, but from existing contextual knowledge and understanding of the broader issues. We leverage high-precision, low-recall techniques in order to automatically build a large corpus of inferential statements related to the text’s topic. These statements are then used to produce a matrix representing the inferential relationship between different aspects of the topic. From this matrix, we are able to determine connectedness and directionality of inference between statements in the original text. By following this approach, we obtain results that compare favourably to those of other similar techniques to classify premise-conclusion pairs (with results 22 points above baseline), but without the requirement of large volumes of annotated, domain specific data.

In this paper we consider the insights that can be gained by considering large scale argument networks and the complex interactions between their constituent propositions. We investigate metrics for analysing properties of these networks, illustrating these using a corpus of arguments taken from the 2016 US Presidential Debates. We present techniques for determining these features directly from natural language text and show that there is a strong correlation between these automatically identified features and the argumentative structure contained within the text. Finally, we combine these metrics with argument mining techniques and show how the identification of argumentative relations can be improved by considering the larger context in which they occur.

Governments are increasingly utilising online platforms in order to engage with, and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enormous feedback from society, they first face the challenge of making sense out of the large volumes of data produced. This creates a demand for tools and technologies which will enable governments to quickly and thoroughly digest the points being made and to respond accordingly. By determining the argumentative and dialogical structures contained within a debate, we are able to determine the issues which are divisive and those which attract agreement. This paper proposes a method of graph-based analytics which uses properties of graphs representing networks of arguments pro- & con- in order to automatically analyse issues which divide citizens about new regulations. By future application of the most recent advances in argument mining, the results reported here will have a chance to scale up to enable sense-making of the vast amount of feedback received from citizens on directions that policy should take.