Authors:
Kenshin Nakanishi
1
;
Tomoyuki Maekawa
2
and
Michita Imai
1
Affiliations:
1
Keio University, Yokohama, Kanagawa, Japan
;
2
Shizuoka University, Shizuoka, Japan
Keyword(s):
Dialogue Context Understanding, Online Dialogue, Missed Utterances, Large Language Model, Natural Language Processing.
Abstract:
When a listener becomes distracted and misses an important utterance, it can hinder their understanding of the conversation and their subsequent responses. In this study, we developed a chat system that simulates the impact of missed important utterances using an algorithm that identifies contextually significant dialogue, which we have been researching previously. The system assesses whether each user utterance contains important context and, if so, notifies the user to alert them of the possibility of misunderstanding by the other party. The results showed that when important utterances were missed, the listener often misunderstood the flow of the conversation. However, the effectiveness of the assistance that alerts users to potential misunderstandings varied depending on the case, and it became clear that the benefits of this feature in a chat system are limited.