Reimagining Intent Prediction: Insights from Graph-Based Dialogue Modeling and Sentence Encoders

Abstract

This paper presents a innovative approach tailored to the specific characteristics of closed-domain dialogue systems. Leveraging scenario dialog graphs, our method effectively addresses the challenges posed by highly specialized fields, where context comprehension is of paramount importance. By modeling dialogues as sequences of transitions between intents, representing distinct goals or requests, our approach focuses on accurate intent prediction for generating contextually relevant responses. The study conducts a thorough evaluation, comparing the performance of state-of-the-art sentence encoders in conjunction with graph-based models across diverse datasets encompassing both open and closed domains. The results highlight the superiority of our methodology, offering fresh perspectives on the integration of advanced sentence encoders and graph models for precise and contextually-driven intent prediction in dialogue systems. Additionally, the use of this approach enhances the transparency of generated output, enabling a deeper understanding of the reasoning behind system responses. This study significantly advances the field of dialogue systems, providing valuable insights into the effectiveness and potential limitations of the proposed approaches.

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