Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking

TL;DR

This paper introduces a dual slot selector mechanism for dialogue state tracking that efficiently determines which slots to update or inherit, significantly improving accuracy and achieving state-of-the-art results on multiple datasets.

Contribution

The paper proposes a novel two-stage dual slot selector for dialogue state tracking that reduces redundant updates and enhances accuracy by leveraging local reliability verification.

Findings

Achieves state-of-the-art joint accuracy on MultiWOZ datasets.

Reduces errors by selectively updating slots based on relevance and reliability.

Significantly improves dialogue state tracking performance.

Abstract

The goal of dialogue state tracking (DST) is to predict the current dialogue state given all previous dialogue contexts. Existing approaches generally predict the dialogue state at every turn from scratch. However, the overwhelming majority of the slots in each turn should simply inherit the slot values from the previous turn. Therefore, the mechanism of treating slots equally in each turn not only is inefficient but also may lead to additional errors because of the redundant slot value generation. To address this problem, we devise the two-stage DSS-DST which consists of the Dual Slot Selector based on the current turn dialogue, and the Slot Value Generator based on the dialogue history. The Dual Slot Selector determines each slot whether to update slot value or to inherit the slot value from the previous turn from two aspects: (1) if there is a strong relationship between it and the current turn dialogue utterances; (2) if a slot value with high reliability can be obtained for it through the current turn dialogue. The slots selected to be updated are permitted to enter the Slot Value Generator to update values by a hybrid method, while the other slots directly inherit the values from the previous turn. Empirical results show that our method achieves 56.93%, 60.73%, and 58.04% joint accuracy on MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.2 datasets respectively and achieves a new state-of-the-art performance with significant improvements.