Dialogue act classification using two multi-party discussion corpora

Abstract

Dialogue act classification is an important task to summarize and analyze discussions. This paper first annotates dialogue act tags to a Japanese multi-party discussion corpus. The tag set is based on an existing multi-party conversation corpus. Then, we propose a multi-dataset learning model for dialogue act classification. In this method, the model is trained from two corpora at the same time. As another approach, we generate a model from the dataset combined from two corpora because the two corpora use the same tag set. We compare the model with multi-dataset learning. The experimental result shows the importance of the corpus size for the task.