LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking

TL;DR

LUNA introduces a slot-utterance alignment method for dialogue state tracking that improves accuracy by focusing on relevant dialogue turns and modeling slot correlations, achieving state-of-the-art results.

Contribution

The paper proposes a novel slot-utterance alignment approach with a slot ranking auxiliary task, enhancing dialogue state tracking performance.

Findings

Achieves new state-of-the-art results on MultiWOZ datasets.

Effectively aligns slots with relevant utterances, reducing noise from irrelevant dialogue history.

Demonstrates improved accuracy over existing methods.

Abstract

Dialogue state tracking (DST) aims to predict the current dialogue state given the dialogue history. Existing methods generally exploit the utterances of all dialogue turns to assign value for each slot. This could lead to suboptimal results due to the information introduced from irrelevant utterances in the dialogue history, which may be useless and can even cause confusion. To address this problem, we propose LUNA, a sLot-tUrN Alignment enhanced approach. It first explicitly aligns each slot with its most relevant utterance, then further predicts the corresponding value based on this aligned utterance instead of all dialogue utterances. Furthermore, we design a slot ranking auxiliary task to learn the temporal correlation among slots which could facilitate the alignment. Comprehensive experiments are conducted on multi-domain task-oriented dialogue datasets, i.e., MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.2. The results show that LUNA achieves new state-of-the-art results on these datasets.