Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 32 of its 46 references that are in the map. First posted on arXiv 27 Jul 2021.
Builds on
32 of 46 references.
References · 32
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20 more
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- 2019Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Trackingcited by 75
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Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 0; 2026: 1.
5 papers.
- 2021PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generationcited by 37
- 2022Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Trackingcited by 6
- 2024State Value Generation with Prompt Learning and Self-Training for Low-Resource Dialogue State Trackingcited by 0
- 2024Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratchcited by 0
- 2026ESAinsTOD: A Unified End-to-End Schema-Aware Instruction-Tuning Framework for Task-Oriented Dialog Modelingcited by 0
- 2026ESAinsTOD: A Unified End-to-End Schema-Aware Instruction-Tuning Framework for Task-Oriented Dialog Modeling10 Mar 2026
- 2024Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch24 Oct 2024
- 2024State Value Generation with Prompt Learning and Self-Training for Low-Resource Dialogue State Tracking30 Jan 2024
- 2022Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking20 May 2022
- 2021PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation20 Sep 2021