Have my arguments been replied to? Argument Pair Extraction as Machine Reading Comprehension (2022)

Abstract: Argument pair extraction (APE) aims to automatically mine argument pairs from two interrelated argumentative documents. Existing studies typically identify argument pairs indirectly by predicting sentence-level relations between two documents, neglecting the modeling of the holistic argument-level interactions. Towards this issue, we propose to address APE via a machine reading comprehension (MRC) framework with two phases. The first phase employs an argument mining (AM) query to identify all arguments in two documents. The second phase considers each identified argument as an APE query to extract its paired arguments from another document, allowing to better capture the argument-level interactions. Also, this framework enables these two phases to be jointly trained in a single MRC model, thereby maximizing the mutual benefits of them. Experimental results demonstrate that our approach achieves the best performance, outperforming the state-of-the-art method by 7.11% in F1 score.

## introduction as a salient part of argument mining (am), the analysis of dialogical argumentation has received increasing research attention @xcite @xcite @xcite . argument pair extraction (ape), proposed by @xcite , is a new task within this field that focuses on extracting interactive argument pairs from two interrelated documents (e.g., peer reviewer and rebuttal). figure 1 presents an example of ape where two interrelated documents are segmented into arguments and non-arguments at sentence level. two arguments from different documents that discuss the same issues are regarded as an argument pair. figure 1: a simplified example of ape task, where each dashed line in the two documents denotes a sentence. s i j is the j-th sentence in document i, and arg i j is an argument in the j-th argument pair from document i. sentences without colors indicate non-arguments, while sentences covered by colors can form arguments. two arguments with the same color are regarded as an argument pair. previous works @xcite commonly address ape by decomposing it into two sentence-level subtasks, i.e., a sequence labeling task and a sentence relation classification task. these methods identify arguments by sentencelevel sequence labeling and determine whether two sentences belong to the same argument pair by sentence relation classification. afterwards, the argument pairs are inferred indirectly by certain rules combining the results of the two subtasks. however, such a paradigm only considers sentencelevel relations, while the holistic argument-level relations can not be well modeled. in this paper, we argue that ape can be considered as a multi-turn machine reading comprehension (mrc) task with two phases, i.e., an am phase and an ape phase. specifically, in the first turn, a special am query is employed to identify all the arguments in the first document (am phase). afterwards, in each subsequent turn, every identified argument is treated as an ape query to extract its paired arguments from the second document (ape phase). similarly, this process can also be performed in another direction, that is, using the arguments identified in the second document as queries to extract the paired arguments from the first document. we train these two phases jointly in a single mrc model, allowing them to benefit each other. by considering arguments as queries, our proposed mrc framework can better capture the interactions between each query argument and the queried document, thus extracting the argument pairs at the argument level. in addition, considering the long length of the documents, we utilize longformer @xcite to model longer contexts. we evaluate our method on the large benchmark dataset @xcite . results show that our proposed method significantly outperforms the current state-of-the-art method by 7.11% in f 1 score. ## conclusion in this paper, we propose to frame the argument pair extraction (ape) task as a machine reading comprehension (mrc) task. our mrc framework addresses ape through two phases with two types of queries, that is, argument mining (am) query and argument pair extraction (ape) query. our proposed method can better model the argumentlevel interactions, thus facilitating the extraction of argument pairs. experimental results on a large benchmark dataset demonstrate that our proposed method achieves state-of-the-art performance.