Beyond Recognising Entailment: Formalising Natural Language Inference from an Argumentative Perspective

Anonymous

Abstract: In argumentation theory, argument schemes provide a foundation that offers a characterisation of stereotypical patterns of inference. There has been little work done in providing computational approaches to identify these schemes in natural language. Moreover, advancements in recognizing textual entailment lack a standardized definition, which makes it challenging to compare methods trained on different datasets. 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. 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.

Paper Type: long

Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Contribution Types: Publicly available software and/or pre-trained models, Data resources, Data analysis

Languages Studied: English