A Simple Contrastive Learning Framework for Interactive Argument Pair Identification via Argument-Context Extraction (2022)

Abstract: Interactive argument pair identification is an emerging research task for argument mining, aiming to identify whether two arguments are interactively related. It is pointed out that the context of the argument is essential to improve identification performance. However, current context-based methods achieve limited improvements since the entire context typically contains much irrelevant information. In this paper, we propose a simple contrastive learning framework to solve this problem by extracting valuable information from the context. This framework can construct hard argument-context samples and obtain a robust and uniform representation by introducing contrastive learning. We also propose an argument-context extraction module to enhance information extraction by discarding irrelevant blocks. The experimental results show that our method achieves the state-of-the-art performance on the benchmark dataset. Further analysis demonstrates the effectiveness of our proposed modules and visually displays more compact semantic representations.

## introduction computational argumentation, as a branch of natural language understanding, has become a new research field. existing work can be divided into two categories (asterhan and schwarz, 2007): monological argumentation and dialogical argumentation. monological argumentation is the scenario for one participant, such as rct @xcite , student essays @xcite and user comments @xcite . the researchers focus on topics like argumentation (argument) mining @xcite figure 1 : an instance in the dataset. each instance includes six arguments: a quotation and its corresponding five candidate replies. additionally, the task provides contextual information for each argument. for this task, the model needs to identify whether the quotation and the reply are interactively related. only one of the five candidate replies is correct. the arguments are represented in green font, and the context is expressed in black. 2020; @xcite , argument assessment @xcite , and argument reasoning @xcite @xcite for this sort of study. recently, researchers have been paying great attention to dialogical argumentation, since online forums have become the primary medium for argumentation and discussion. people can express themselves on the network anywhere and at any time, thanks to the widespread use of the internet and communication technologies. indeed, different people have diverse arguments on a subject, and argumentation is the most effective way to interchange arguments. many online forums, such as changemyview foot_0 and idebate foot_1 , provide a venue for free online argumentation, allowing users to argue with others regardless of time or location. therefore, the study of argumentation in the interactive text arises. earlier research @xcite uses the data from the changemyview forum to focus on the key elements of persuasion arguments. then, @xcite formulates an interesting and meaningful task to identify whether two arguments are interactively related. more interestingly, @xcite have applied the task to the legal field to help the court to pinpoint the focus of the case by analyzing the arguments of both sides in the trial transcript and allow the judge to make a fair decision. figure 1 demonstrates the details of this task. obviously, it is difficult to identify the interactive relationship by two arguments because most arguments contain only a few words. moreover, contextual information is related to the meaning of the quotation and reply. thus, it is essential to utilize contexts. @xcite propose a hierarchical rnn network to model context. @xcite constructs the argumentation knowledge graph to extract entity information from the context. however, the current context-based methods achieve limited improvement since the entire context normally contains a large amount of irrelevant information. figure 2 shows the heatmaps of the semantic similarity between the argument and each sentence in its context for the instance of figure 1 . remarkably, many sentences in the context have very low semantic similarity to the argument, and some are even close to 0. intuitively, as shown in figure 1 , the quotation talks about "terrorists deserve justice in court" while its context mentions "religion", "drones", and other irrelevant information. "drones" can also be found in the contexts of the two negative replies. if the whole context is modeled, the model is likely to infer the interactive relationship between quotation and negative reply2. undoubtedly, these irrelevant sentences are noisy data for this task, negatively affecting the model training. in this paper, we propose a simple contrastive learning framework to enhance the robustness of the model under noise conditions and reduce the adverse effects on model. this framework can construct hard argument-context samples by randomly extracting the context blocks. we combine the cross-entropy and the supervised contrastive loss to improve the expressiveness of the representations. in addition, we propose an argument-context extraction (ace) module to enhance context information extraction. in this module, we can obtain the semantic similarity of argument and context blocks and further extract the context blocks with high similarity as the model's input. through empirical analysis, we observe that our model performs better with the benchmark dataset and noisy dataset, which proves the superiority of our method. our main contributions can be summarized as follows: