Graph Models for Contextual Intention Prediction in Dialog Systems

Abstract

The paper introduces a novel methodology for predicting intentions in dialog systems through a graph-based approach. This methodology involves constructing graph structures that represent dialogs, thus capturing contextual information effectively. By analyzing results from various open and closed domain datasets, the authors demonstrate the substantial enhancement in intention prediction accuracy achieved by combining graph models with text encoders. The primary focus of the study revolves around assessing the impact of diverse graph architectures and encoders on the performance of the proposed technique. Through empirical evaluation, the experimental outcomes affirm the superiority of graph neural networks in terms of both \(Recall@k\) (MAR) metric and computational resources when compared to alternative methods. This research uncovers a novel avenue for intention prediction in dialog systems by leveraging graph-based representations.