mlsanityData to 14 Oct 2026

On Tracking Dialogue State by Inheriting Slot Values in Mentioned Slot Pools

arXiv 2202.07156 ↗First posted 15 Feb 2022cs.CL cs.AIcited by 5

Not on the map. Why?

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

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  4. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,475
  5. 2020A Simple Language Model for Task-Oriented Dialoguecited by 332
  6. 2018Bilinear Attention Networkscited by 280
  7. 2016A Network-based End-to-End Trainable Task-oriented Dialogue Systemcited by 252
  8. 2019Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systemscited by 216
  9. 2020MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselinescited by 204
  10. 2020TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Trackingcited by 130
  11. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  12. 2020DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialoguecited by 89
12 more
  1. 2019Efficient Dialogue State Tracking by Selectively Overwriting Memorycited by 76
  2. 2019Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Trackingcited by 75
  3. 2019SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Trackingcited by 63
  4. 2019Dialog State Tracking: A Neural Reading Comprehension Approachcited by 62
  5. 2020CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackerscited by 43
  6. 2020A Sequence-to-Sequence Approach to Dialogue State Trackingcited by 43
  7. 2019Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answeringcited by 39
  8. 2019BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformercited by 38
  9. 2021Amendable Generation for Dialogue State Trackingcited by 21
  10. 2021Effective Sequence-to-Sequence Dialogue State Trackingcited by 20
  11. 2018Toward Scalable Neural Dialogue State Tracking Modelcited by 11
  12. 2020Data Augmentation for Copy-Mechanism in Dialogue State Trackingcited by 7

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 2.

5 papers.