An Adversarial Learning based Multi-Step Spoken Language Understanding System through Human-Computer Interaction

TL;DR

This paper presents a novel adversarial learning-based multi-step spoken language understanding system that effectively utilizes multi-round user feedback to improve semantic parsing accuracy in dialogue systems.

Contribution

The paper introduces a new multi-step SLU system leveraging adversarial learning to incorporate multi-round user feedback, enhancing parsing performance over existing single-round systems.

Findings

Improves F1 score by at least 2.5% with one feedback round

Performance gains increase with more feedback rounds

Outperforms state-of-the-art dialogue state tracking systems on multiround tasks

Abstract

Most of the existing spoken language understanding systems can perform only semantic frame parsing based on a single-round user query. They cannot take users' feedback to update/add/remove slot values through multiround interactions with users. In this paper, we introduce a novel multi-step spoken language understanding system based on adversarial learning that can leverage the multiround user's feedback to update slot values. We perform two experiments on the benchmark ATIS dataset and demonstrate that the new system can improve parsing performance by at least $2.5\%$ in terms of F1, with only one round of feedback. The improvement becomes even larger when the number of feedback rounds increases. Furthermore, we also compare the new system with state-of-the-art dialogue state tracking systems and demonstrate that the new interactive system can perform better on multiround spoken language understanding tasks in terms of slot- and sentence-level accuracy.