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Measuring the similarity between two different sentential arguments is animportant task in argument mining. However, one of the challenges in this fieldis that the dataset must be annotated using expertise in a variety of topics,making supervised learning with labeled data expensive. In this paper, weinvestigated whether this problem could be alleviated through transferlearning. We first adapted a pretrained language model to a domain of interestusing self-supervised learning. Then, we fine-tuned the model to a task ofmeasuring the similarity between sentences taken from different domains. Ourapproach improves a correlation with human-annotated similarity scores comparedto competitive baseline models on the Argument Facet Similarity dataset in anunsupervised setting. Moreover, we achieve comparable performance to a fullysupervised baseline model by using only about 60% of the labeled data samples.We believe that our work suggests the possibility of a generalized argumentclustering model for various argumentative topics.

An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation