  Measuring the similarity between two different sentential arguments is an
important task in argument mining. However, one of the challenges in this field
is that the dataset must be annotated using expertise in a variety of topics,
making supervised learning with labeled data expensive. In this paper, we
investigated whether this problem could be alleviated through transfer
learning. We first adapted a pretrained language model to a domain of interest
using self-supervised learning. Then, we fine-tuned the model to a task of
measuring the similarity between sentences taken from different domains. Our
approach improves a correlation with human-annotated similarity scores compared
to competitive baseline models on the Argument Facet Similarity dataset in an
unsupervised setting. Moreover, we achieve comparable performance to a fully
supervised baseline model by using only about 60% of the labeled data samples.
We believe that our work suggests the possibility of a generalized argument
clustering model for various argumentative topics.
