mlsanityData to 7 Oct 2026

Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?

arXiv 2105.03571 ↗First posted 8 May 2021cs.CLcited by 5

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Abstract (arXiv)

Dialogue state tracking (DST) plays a key role in task-oriented dialogue systems to monitor the user's goal. In general, there are two strategies to track a dialogue state: predicting it from scratch and updating it from previous state. The scratch-based strategy obtains each slot value by inquiring all the dialogue history, and the previous-based strategy relies on the current turn dialogue to update the previous dialogue state. However, it is hard for the scratch-based strategy to correctly track short-dependency dialogue state because of noise; meanwhile, the previous-based strategy is not very useful for long-dependency dialogue state tracking. Obviously, it plays different roles for the context information of different granularity to track different kinds of dialogue states. Thus, in this paper, we will study and discuss how the context information of different granularity affects dialogue state tracking. First, we explore how greatly different granularities affect dialogue state tracking. Then, we further discuss how to combine multiple granularities for dialogue state tracking. Finally, we apply the findings about context granularity to few-shot learning scenario. Besides, we have publicly released all codes.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 24 references that are in the map. First posted on arXiv 8 May 2021.

Builds on

21 of 24 references.

References · 21

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9 more
  1. 2019BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformercited by 37
  2. 2018An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Trackingcited by 27
  3. 2020Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Trackingcited by 27
  4. 2017Scalable Multi-Domain Dialogue State Trackingcited by 22
  5. 2019Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generationcited by 21
  6. 2020A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Trackingcited by 20
  7. 2018Multi-task learning for Joint Language Understanding and Dialogue State Trackingcited by 15
  8. 2019HyST: A Hybrid Approach for Flexible and Accurate Dialogue State Trackingcited by 14
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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 2.

5 papers.

  1. 2021Contextual Semantic Parsing for Multilingual Task-Oriented Dialoguescited by 10
  2. 2022Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Trackingcited by 6
  3. 2023Diable: Efficient Dialogue State Tracking as Operations on Tablescited by 4
  4. 2022Prompt-based Generative Approach towards Multi-Hierarchical Medical Dialogue State Trackingcited by 2
  5. 2023Turn-Level Active Learning for Dialogue State Trackingcited by 0