Identifying Discourse Boundaries in Group Discussions using a Multimodal Embedding Space

In group discussion, it is not always easy for the participants to effectively control the discussion to make it fruitful. With the goal of contributing to facilitating group discussions, this study proposes a method of segmenting a discussion. Predicted discussion boundaries may be useful for tracking the discussion topics, analyzing the discussion structure, and determining a timing for intervention. We created a multimodal embedding space using an autoencoder, and represented each multimodal utterance data in the embedding space. Then, a simple unsupervised approach was used to detect the discussion boundary. In a preliminary analysis, we found that the proposed method can generate discussion segments that are comprehensible for analyzing a discourse structure. But, the performance in the discourse segmentation task should be improved as future work. Author