An Utterance is Enough to the gaze? Gaze Detection from Utterance Information in Multiparty Discussion

Gaze information has an important role in conversation understanding. Generally, gaze detection is performed using a camera. However, such systems often require expensive equipment. To solve this problem, we propose a method of eye detection using an utterance during conversations. We first annotate an existing discussion corpus with gaze information and then analyze the annotated data. We apply machine learning to construct a gaze detection model. We utilize BERT, a large-scale pre-trained language model, and CART, a traditional machine-learning approach based on a decision tree. Experimental results show that the CART-based model was more accurate than the BERT-based model and show the potential possibilities of gaze detection from utterances.