Emotion Analysis and Dialogue Breakdown Detection in Dialogue of Chat Systems Based on Deep Neural Networks

Abstract

In dialogues between robots or computers and humans, dialogue breakdown analysis is an important tool for achieving better chat dialogues. Conventional dialogue breakdown detection methods focus on semantic variance. Although these methods can detect dialogue breakdowns based on semantic gaps, they cannot always detect emotional breakdowns in dialogues. In chat dialogue systems, emotions are sometimes included in the utterances of the system when responding to the speaker. In this study, we detect emotions from utterances, analyze emotional changes, and use them as the dialogue breakdown feature. The proposed method estimates emotions by utterance unit and generates features by calculating the similarity of the emotions of the utterance and the emotions that have appeared in prior utterances. We employ deep neural networks using sentence distributed representation vectors as the feature. In an evaluation of experimental results, the proposed method achieved a higher dialogue breakdown detection rate when compared to the method using a sentence distributed representation vectors.

Conversational Repair Strategies to Cope with Errors and Breakdowns in Customer Service Chatbot Conversations

“…on its quality, potentially resulting in conversation breakdowns' [34, p.151]. Users frequently experience conversational breakdowns during interactions with chatbots, as the chatbot often struggles to accurately interpret (complex) requests or understand the intended meaning [9,23,24,29,36]. Conversational breakdowns may frustrate users, diminishing their trust in chatbots [2,11,37] while also reducing user satisfaction and willingness to continue using a chatbot [1].…”