Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph (2021)

Abstract: Argument pair extraction (APE) aims to extract interactive argument pairs from two passages of a discussion. Previous work studied this task in the context of peer review and rebuttal, and decomposed it into a sequence labeling task and a sentence relation classification task. However, despite the promising performance, such an approach obtains the argument pairs implicitly by the two decomposed tasks, lacking explicitly modeling of the argument-level interactions between argument pairs. In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage. In this manner, two passages can mutually guide each other in the process of APE. Furthermore, we propose an inter-sentence relation graph to effectively model the inter-relations between two sentences and thus facilitates the extraction of argument pairs. Our proposed method can better represent the holistic argument-level semantics and thus explicitly capture the complex correlations between argument pairs. Experimental results show that our approach significantly outperforms the current state-of-the-art model.

## introduction argumentation mining has received increasing research attention in recent years. existing studies can be categorized into monological argumentation @xcite @xcite and dialogical argumentation @xcite @xcite , with the former identifying the argumentation structure of a single monological document, and the latter fo- * corresponding author cusing on the analysis of argumentation in debates or discussions. argument pair extraction (ape) is a new task within the field of dialogical argumentation, aiming at extracting interactive argument pairs from two argumentative passages of a discussion. @xcite investigated this task in the context of peer review and rebuttal, as they involve rich argumentative and interactive discussions. an example of ape is shown in figure 1 , where a review passage and its corresponding rebuttal passage are segmented into arguments and non-arguments at sentence level. the arguments in review can form argument pairs with the arguments in rebuttal, according to the points they discuss. ape is a highly challenging task because we need to understand not only the argumentation structure presented by each side of the discussion, but also the interaction of arguments between the participants. the interactions between arguments can be complicated, for example, one argument may be paired with multiple other arguments, forming one-to-many relations. this task is essential for understanding the structure of dialogical argumentation and can also support other related tasks, such as argument generation @xcite and debate summarization @xcite . due to the rich interaction of complex arguments, peer review and rebuttal are perfect resources for ape, and have also been exploited in other tasks @xcite . @xcite proposed to tackle ape by decomposing it into a sequence labeling task and a sentence relation classification task, with the first subtask extracting the arguments in each review or rebuttal, and the second subtask determining whether two sentences belong to the same pair of arguments. these two subtasks are jointly optimized within a multi-task learning framework, and this work applies convolutional neural networks to the task of rgb-d indoor scene segmentation. the model simply adds depth as a separate channel to the existing rgb channels in a conv net. depth has some unique properties e.g. infinity/ missing values depending on the sensor. ## proposed approach we present a mutual guidance framework with an inter-sentence relation graph for ape, named mgf. our approach can better utilize the holistic argument-level semantics and thus explicitly capture the complex correlations between argument pairs. the overall architecture is shown in figure 2 . in the following, we first introduce the intersentence relation graph, then describe the mutual guidance framework. ## related work most existing studies in the field of argumentaion mining focus on monological argumentation, such as argumentation structure parsing @xcite @xcite @xcite , automated essay scoring @xcite @xcite , argument quality assessment @xcite @xcite , argumentation strategies modeling @xcite , etc. since real-life argumentation is usually in the form of dialogue, some prior work focuses on dialogical argumentation. @xcite employed a pointer network to predict argumentation structures in discussion threads. @xcite studied the relations between argument components in online discussion forums with pre-trained models and discourse relations. @xcite proposed a discrete argument representation learning method to extract argument pairs. however, these studies above assumed that the boundaries of arguments have been given. recently, @xcite present a new task named argument pair extraction, which is more challenging as it requires both identifying arguments from plain text and extracting the interactive argument pairs. our work is closely related to the argument relation prediction task. many studies of argumentation structure parsing include argumentative relation prediction as a subtask @xcite @xcite . since argument relation prediction is highly challenging, recently, more and more researchers study it as an independent task @xcite @xcite . despite the strong connection, ape task is more challenging than argument relation prediction. specifically, in argument relation prediction, arguments are given. but for ape, only two plain documents without any pre-labeled information are given, and we need to identify arguments in two documents and determine argument relations simultaneously. graph neural networks (gnn) have shown promising performance in many nlp tasks, such as text classification @xcite , question answering @xcite , sentiment analysis @xcite ), text summarization @xcite , etc. recently, some works have attempted to introduce gnn into argumentation mining. @xcite ## conclusion in this paper, we propose an effective mutual guidance framework for argument pair extraction, named mgf, which enables arguments of two passages to mutually guide each other for extracting interactive argument pairs. in addition, we introduce an inter-sentence relation graph into our proposed mgf, which could effectively model the inter-relations between two sentences and thus improving the extraction of argument pairs. the experimental results demonstrate the effectiveness of our method. in the future, we plan to apply our method to datasets from more diverse domains beyond the peer review and rebuttal, such as social networks, debate competitions, etc.