On Tracking Dialogue State by Inheriting Slot Values in Mentioned Slot Pools
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Abstract (arXiv)
Dialogue state tracking (DST) is a component of the task-oriented dialogue system. It is responsible for extracting and managing slot values according to dialogue utterances, where each slot represents an essential part of the information to accomplish a task, and slot value is updated recurrently in each dialogue turn. However, many DST models cannot update slot values appropriately. These models may repeatedly inherit wrong slot values extracted in previous turns, resulting in the fail of the entire DST task. They cannot update indirectly mentioned slots well, either. This study designed a model with a mentioned slot pool (MSP) to tackle the update problem. The MSP is a slot-specific memory that records all mentioned slot values that may be inherited, and our model updates slot values according to the MSP and the dialogue context. Our model rejects inheriting the previous slot value when it predicates the value is wrong. Then, it re-extracts the slot value from the current dialogue context. As the contextual information accumulates with the dialogue progress, the new value is more likely to be correct. It also can track the indirectly mentioned slot by picking a value from the MSP. Experimental results showed our model reached state-of-the-art DST performance on MultiWOZ 2.1 and 2.2 datasets.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 24 of its 29 references that are in the map. First posted on arXiv 15 Feb 2022.
Builds on
24 of 29 references.
References · 24
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12 more
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Built on by
Citing papers per year: 2022: 2; 2023: 1; 2024: 2.
5 papers.
- 2023Towards LLM-driven Dialogue State Trackingcited by 25
- 2024Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulationcited by 24
- 2022DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learningcited by 3
- 2022MetaASSIST: Robust Dialogue State Tracking with Meta Learningcited by 3
- 2024Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Trackingcited by 2
- 2024Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation17 May 2024
- 2024Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking12 Mar 2024
- 2023Towards LLM-driven Dialogue State Tracking23 Oct 2023
- 2022MetaASSIST: Robust Dialogue State Tracking with Meta Learning22 Oct 2022
- 2022DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning25 May 2022