The Discussion Corpus toward Argumentation Quality Assessment in Multi-Party Conversation
Tsukasa Shiota, Kazutaka Shimada · 2020
Recently, many studies in NLP, especially argument mining, have focused on revealing what a good argument is with written texts. However, there are a few studies to challenge argumentation quality assessment in face-to-face conversations due to a lack of corpora, as compared with written texts. Therefore, we construct a multimodal multi-party discussion corpus toward argumentation quality assessment in multi-party conversation. The corpus consists of 10 multi-party discussions (200 minutes) in total. Each participant is annotated for 6 scores about rhetoric, which is one of the classes of argumentation quality. In this paper, we explain the procedure of our corpus construction and report the results of the annotation.