Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation
This work addresses the data scarcity problem in Argument Mining for researchers and practitioners by leveraging a large corpus and evaluating transformer models.
This paper explores the use of transformer-based models for automatically identifying argument relations (support, attack, rephrase, no relation) using the large US2016 debate corpus. The models achieved a macro F1-score of 0.70 on the US2016 corpus and 0.61 on the Moral Maze cross-domain corpus.
Argument Mining is defined as the task of automatically identifying and extracting argumentative components (e.g., premises, claims, etc.) and detecting the existing relations among them (i.e., support, attack, rephrase, no relation). One of the main issues when approaching this problem is the lack of data, and the size of the publicly available corpora. In this work, we use the recently annotated US2016 debate corpus. US2016 is the largest existing argument annotated corpus, which allows exploring the benefits of the most recent advances in Natural Language Processing in a complex domain like Argument (relation) Mining. We present an exhaustive analysis of the behavior of transformer-based models (i.e., BERT, XLNET, RoBERTa, DistilBERT and ALBERT) when predicting argument relations. Finally, we evaluate the models in five different domains, with the objective of finding the less domain dependent model. We obtain a macro F1-score of 0.70 with the US2016 evaluation corpus, and a macro F1-score of 0.61 with the Moral Maze cross-domain corpus.