Leveraging Argumentation Knowledge Graph for Interactive Argument Pair Identification

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2310–2319 August 1–6, 2021. ©2021 Association for Computational Linguistics 2310 Leveraging Argumentation Knowledge Graph for Interactive Argument Pair Identification Jian Yuan1, Zhongyu Wei1,2∗, Donghua Zhao3, Qi Zhang4, Changjian Jiang5∗ 1School of Data Science, Fudan University, China 2Research Institute of Intelligent and Complex Systems, Fudan University, China 3School of Mathematical Sciences, Fudan University, China 4School of Computer Science, Fudan University, China 5School of International Relations & Public Affairs, Fudan University, China {19210980107,zywei,dhzhao,qz,Changjian}@fudan.edu.cn Abstract Interactive argument pair identification is es- sential in the context of dialogical argumenta- tion mining. Existing research treats it as a problem of sentence matching and largely re- lies on textual information to compute the sim- ilarities. 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 argu- ment 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 prac- tice, we utilize Graph Convolutional Network (GCN) to learn the concept representation in the knowledge graph and use a Transformer- based encoder to learn the representation of paths. Finally, an information alignment net- work is employed to capture the interaction of textual information of conceptual informa- tion (both entity-level and path-level). Exper- iment results indicate that our model achieves state-of-the-art performance in the benchmark dataset. Further analysis demonstrates the ef- fectiveness of our model for enforcing knowl- edge reasoning through paths in the knowl- edge graph. 1 Introduction Argumentation Mining aims at analyzing the se- mantic and logical structure of argumentative texts. Existing research covers argument structure predic- tion (Morio et al., 2020; Li et al., 2020), persua- siveness evaluation (Al Khatib et al., 2020; El Baff et al., 2020) and argument summarization (Bar- Haim et al., 2020b,a). Most of them focus on mono- ∗Corresponding author. Figure 1: Two instances of interactive argument pairs, the related concepts are colored same, and the corre- sponding knowledge is visualised in the right side. logical context like student essays, public speeches, etc., where only one participant is involved. Online forums such as idebate1 and change- myview2, enable people to exchange opinions on some specific topics freely. The user generated dataset of interactive arguments also motivates an- other line of research for argumentation in dialog- ical context (Asterhan and Schwarz, 2007). Ini- tial researches in this filed focused on analyzing the ChangeMyView data (Tan et al., 2016; Wei et al., 2016) to summarize the key factors of per- suasive arguments. Furthermore, Ji et al. (2019) and Cheng et al. (2020) propose the task of identi- fying and extracting interactive arguments. Ji et al. (2019) formulate this task as a problem of sentence pair scoring and computes the textual similarity 1https://idebate.org/ 2https://www.reddit.com/r/ changemyview/ 2311 between …

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