In argumentation theory, argument schemes are a characterisation of stereotypical patterns of inference.There has been little work done to develop computational approaches to identify these schemes in natural language.Moreover, advancements in recognizing textual entailment lack a standardized definition of inference, which makes it challenging to compare methods trained on different datasets and rely on the generalisability of their results.In this work, we propose a rigorous approach to align entailment recognition with argumentation theory.Wagemans' Periodic Table of Arguments (PTA), a taxonomy of argument schemes, provides the appropriate framework to unify these two fields.To operationalise the theoretical model, we introduce a tool to assist humans in annotating arguments according to the PTA.Beyond providing insights into non-expert annotator training, we present Kialo-PTA24, the first multi-topic dataset for the PTA.Finally, we benchmark the performance of pre-trained language models on various aspects of argument analysis.Our experiments show that the task of argument canonicalisation poses a significant challenge for state-of-the-art models, suggesting an inability to represent argumentative reasoning and a direction for future investigation.1. We conduct an annotation study to rephrase natural language arguments into structured templates and provide insights into how to train non-expert annotators to perform this analysis.2. We introduce ArgNotator, a tool that assists humans in annotating arguments according to the PTA.3. We construct Kialo-PTA24 -the first multi-topic dataset of argument types annotated according to the PTA.4. We compare the performance of state-of-theart models for two annotation subtasks.For the substance classification task, we benchmark the performance of a number of BERT-based models.For the argument canonicalisation task, we evaluate the performance of two large language models (FLAN-T5, LLAMA2) in both pre-trained and few-shot settings.The dataset, experimental setup, annotation tool and training materials can all be found on GitLab. 1

Argument mining seeks to extract arguments and their structure from unstructured texts.Identifying relations (such as attack, support, and neutral) between argumentative units is a challenging task because two units may be related to each other via implicit inferences.These inferences often rely on external commonsense knowledge to discover how one argumentative unit relates to another.State-of-the-art methods, however, rely on predefined knowledge graphs, and thus might not cover target pairs of argumentative units well.We introduce a new generative approach to finding inference chains that connect these pairs by making use of the Commonsense Transformer (COMET).We evaluate our approach on three datasets for both the two-label (attack/support) and three-label (attack/support/neutral) tasks.Our approach significantly outperforms the state-of-the-art, by 2-5% in F1 score, on two out of the three datasets with minor improvements on the remaining one.