Argumentation scheme mining is the task of automatically identifying reasoning mechanisms behind argument inferences. These mechanisms provide insights into underlying argument structures and guide the assessment of natural language arguments. Research on argumentation scheme mining, however, has always been limited by the scarcity of large enough publicly available corpora containing scheme annotations. In this paper, we present the first state-of-the-art results for mining argumentation schemes in natural language dialogue. For this purpose, we create QT-SCHEMES, a new corpus of 441 arguments annotated with 24 argumentation schemes. Using this corpus, we leverage the capabilities of LLMs and Transformer-based models, pre-training them on a large corpus containing textbook-like argumentation schemes and validating their applicability in real-world scenarios.

Traditionally, argument mining research has approached the task of automatic identification of argument structures by using existing definitions of what constitutes an argument, while leaving the equally important matter of what does not qualify as an argument unaddressed. With the ability to distinguish between what is and what is not a natural language argument being at the core of argument mining as a field, it is interesting that no previous work has explored approaches to effectively select non-related propositions (i.e., propositions that are not connected through an argumentative relation, such as support or attack) that improve the data for learning argument mining tasks better. In this paper, we address the question of how to effectively sample non-related propositions from six different argument mining corpora belonging to different domains and encompassing both monologue and dialogue forms of argumentation. To that end, in addition to considering undersampling baselines from previous work, we propose three new sampling strategies relying on context (i.e., short/long) and the semantic similarity between propositions. Our results indicate that using more informed sampling strategies improves the performance, not only when evaluating models on their respective test splits, but also in the case of cross-domain evaluation.

Argument mining has typically been researched for specific corpora belonging to concrete languages and domains independently in each research work. Human argumentation, however, has domain- and language-dependent linguistic features that determine the content and structure of arguments. Also, when deploying argument mining systems in the wild, we might not be able to control some of these features. Therefore, an important aspect that has not been thoroughly investigated in the argument mining literature is the robustness of such systems to variations in language and domain. In this paper, we present a complete analysis across three different languages and three different domains that allow us to have a better understanding on how to leverage the scarce available corpora to design argument mining systems that are more robust to natural language variations.

An important open challenge in the area of computational argumentation is the automatic reconstruction of natural language enthymemes. Such argumentative figures are commonly used in natural language human discourse to improve the naturalness and efficiency of speech. They also represent a major challenge when developing computational argumentation systems that need to work with natural language data, since enthymemes bring irregularity to the representations proposed in classical models of argumentation. In this paper, we propose a new framework based on the theory of argumentation schemes aimed at automatically reconstructing natural language enthymemes. The proposed framework consists of a two-module pipeline: (i) scheme classification, and (ii) enthymeme reconstruction. We validate the proposed framework by comparing its performance to a baseline pipeline that does not take the argumentation scheme theory into account. We evaluate the framework by analysing the validity of the complete reconstructed arguments, establishing a new set of baselines that can be used as reference for future work in this direction.

Generating and evaluating hypotheses about past, present, and future events is core to argumentation in many domains, such as forensic investigations, medical diagnostics, and scientific research. In this paper, we explore the role of hypothesis-making in argumentative dialogue. To do so, we introduce an annotated dataset of 502 hypotheses in the existing RIP corpus of collaborative problem-solving in murder mystery games, creating the RIP1 corpus. Propositions marked as hypotheses in RIP1 correlate systematically with argument structure (previously annotated according to Inference Anchoring Theory). We explore the interaction between arguments and hypotheses, showing hypotheses are often conclusions of arguments and differences between how hypotheses and assertions are treated in dialogue. Based on this quantitative analysis, we conduct preliminary computational experiments establishing a baseline for the automatic mining of hypotheses. Experimentation with a Support Vector Machine and a fine-tuned RoBERTa model shows initial performance on text span classification with an F1 score of 80.0, outperforming random and majority baselines, and providing a target for future improvement.

In this study, we introduce KIALOPRIME, a novel large-scale dataset comprising 5,687 argument discussion graphs with a total of 1,088,801 of supporting, attacking, and neutral argument relations, derived from the structured debates of the online discussion platform Kialo.com. This dataset facilitates in-depth analysis of argument structures and the dynamics of discourse, serving as a substantial resource for computational argumentation research. We explore argument inference through traditional sequence classification and a modern generative reasoning based approach, employing an open-source mixture of experts LLM to interpret and enrich each argument pair with high-quality synthetic elaborations about the argumentative interaction. We achieve baseline results of F1 .899 and .840 within discussions and F1 .908 and .840 across discussions for the argument relation and elaboration classification models, respectively. While the elaboration-based model scores slightly lower on the classification task, we highlight areas of improvement to better capture the hidden complexities of argumentative text. These initial findings are promising as they not only establish robust benchmarks for future studies but also demonstrate the potential for using generative reasoning to provide a more insightful analysis of argument relations.

Abstract One of the greatest challenges of computational argumentation research consists of creating persuasive strategies that can effectively influence the behaviour of a human user. From the human perspective, argumentation represents one of the most effective ways to reason and to persuade other parties. Furthermore, it is very common that humans adapt their discourse depending on the audience in order to be more persuasive. Thus, it is of utmost importance to take into account user modelling features for personalising the interactions with human users. Through computational argumentation, we can not only devise the optimal solution, but also provide the rationale for it. However, synergies between computational argumentative reasoning and computational persuasion have not been researched in depth. In this paper, we propose a new formal framework aimed at improving the persuasiveness of arguments resulting from the computational argumentative reasoning process. For that purpose, our approach relies on an underlying abstract argumentation framework to implement this reasoning and extends it with persuasive features. Thus, we combine a set of user modelling and linguistic features through the use of a persuasive function in order to instantiate abstract arguments following a user-specific persuasive policy. From the results observed in our experiments, we can conclude that the framework proposed in this work improves the persuasiveness of argument-based computational systems. Furthermore, we have also been able to determine that human users place a high level of trust in decision support systems when they are persuaded using arguments and when the reasons behind the suggestion to modify their behaviour are provided.

The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the inability of approaching more complex natural language processing tasks.Such is the case of the automatic evaluation of complete professional argumentative debates.In this paper, we propose an original hybrid method to automatically predict the winning stance in this kind of debates.For that purpose, we combine concepts from argumentation theory such as argumentation frameworks and semantics, with Transformer-based architectures and neural graph networks.Furthermore, we obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.

In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks.The creation of this corpus represents an important contribution to the intersection of speech processing and argument mining communities, and one of the most complete publicly available resources in this topic.Moreover, we have performed a set of first-of-their-kind experiments which show an improvement when integrating audio features into the argument mining pipeline.The provided results can be used as a baseline for future research.

Notions of rationality and bounded rationality play important roles in research on the design and implementation of autonomous agents and multi-agent systems, for example in the context of instilling socially intelligent behavior into computing systems. However, the (formal) connection between artificial intelligence research on the design and implementation of boundedly rational and socially intelligent agents on the one hand and formal economic rationality – i.e., choice with clear and consistent preferences – or instrumental rationality – i.e., the maximization of a performance measure given an agent’s knowledge – on the other hand is weak. In this paper we address this shortcoming by introducing a formal framework for designing boundedly rational agents that systematically relax instrumental rationality, and we propose a system architecture for implementing such agents.

Argumentation schemes are generalised patterns that provide a way to (partially) dissociate the content from the reasoning structure of the argument. On the other hand, Cialdini’s principles of persuasion provide a generic model to analyse the persuasive properties of human interaction (e.g., natural language). Establishing the relationship between principles of persuasion and argumentation schemes can contribute to the improvement of the argument-based human-computer interaction paradigm. In this work, we perform a qualitative analysis of the persuasive properties of argumentation schemes. For that purpose, we present a new study conducted on a population of over one hundred participants, where twelve different argumentation schemes are instanced into four different topics of discussion considering both stances (i.e., in favour and against). Participants are asked to relate these argumentation schemes with the perceived Cialdini’s principles of persuasion. From the results of our study, it is possible to conclude that some of the most commonly used patterns of reasoning in human communication have an underlying persuasive focus, regardless of how they are instanced in natural language argumentation (i.e., their stance, the domain, or their content).

Computational Argumentation studies the definition of models able to either have a debate, persuade users in decision making or assist humans with argument analysis. In this work, some of our initial contributions and the foundations of this research field are presented.