Leveraging Argumentation Knowledge Graph for Interactive Argument Pair Identification

Abstract

Interactive argument pair identification is essential in the context of dialogical argumentation mining. Existing research treats it as a problem of sentence matching and largely relies on textual information to compute the similarities. However, the interaction of opinions usually involves the background of the topic and requires reasoning of knowledge, which is beyond textual information. In this paper, we propose to leverage external knowledge to enhance the identification of interactive argument pairs. We construct the argumentation knowledge graph from the discussion thread of the target topic in the online forum. The interaction between the original argument and the reply is then represented as the path of concepts in the knowledge graph. In practice, we utilize Graph Convolutional Network (GCN) to learn the concept representation in the knowledge graph and use a Transformerbased encoder to learn the representation of paths. Finally, an information alignment network is employed to capture the interaction of textual information of conceptual information (both entity-level and path-level). Experiment results indicate that our model achieves state-of-the-art performance in the benchmark dataset. Further analysis demonstrates the effectiveness of our model for enforcing knowledge reasoning through paths in the knowledge graph.

Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation

“…Counter-Argument Generation Datasets (Ji et al, 2021;Yuan et al, 2021;Hua and Wang, 2018;Stab et al, 2018) for counter-argument generation mainly establish the rebuttal relationship in the conversation using automatic methods such as citation or reply detection. Tan et al (2016) Topic: How should the struggles of white upper middle-class individuals be addressed in the USA?…”

Teach Me How to Argue: A Survey on NLP Feedback Systems in Argumentation

“…Indeed, supporting or refuting a claim is made of complex logical moves, such as promoting, contradicting, or acknowledging a fact. To identify the different relations patterns, Yuan et al (2021) focus on finding interactive argument pairs, whereas Mim et al ( 2022) enables annotating complex attack relations.…”

Have my arguments been replied to? Argument Pair Extraction as Machine Reading Comprehension

“…Cheng et al (2021) address the APE task based on a table-filling approach. Yuan et al (2021) construct a dialogical argumentation knowledge graph for identifying argument pairs.…”

“…As a salient part of argument mining (AM), the analysis of dialogical argumentation has received increasing research attention (Morio and Fujita, 2018;Chakrabarty et al, 2019;Cheng et al, 2021;Yuan et al, 2021). Argument pair extraction (APE), proposed by Cheng et al (2020), is a new task within this field that focuses on extracting interactive argument pairs from two interrelated documents (e.g., peer reviewer and rebuttal).…”