MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation
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Abstract (arXiv)
The MultiWOZ 2.0 dataset has greatly stimulated the research of task-oriented dialogue systems. However, its state annotations contain substantial noise, which hinders a proper evaluation of model performance. To address this issue, massive efforts were devoted to correcting the annotations. Three improved versions (i.e., MultiWOZ 2.1-2.3) have then been released. Nonetheless, there are still plenty of incorrect and inconsistent annotations. This work introduces MultiWOZ 2.4, which refines the annotations in the validation set and test set of MultiWOZ 2.1. The annotations in the training set remain unchanged (same as MultiWOZ 2.1) to elicit robust and noise-resilient model training. We benchmark eight state-of-the-art dialogue state tracking models on MultiWOZ 2.4. All of them demonstrate much higher performance than on MultiWOZ 2.1.
Key facts. Cited by 86 papers in the map (data through 7 Oct 2026). Builds on 24 of its 33 references that are in the map. First posted on arXiv 1 Apr 2021.
Builds on
24 of 33 references.
References · 24
- 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
- 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 760
- 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 580
- 2019Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Datasetcited by 426
- 2020A Simple Language Model for Task-Oriented Dialoguecited by 331
- 2016A Network-based End-to-End Trainable Task-oriented Dialogue Systemcited by 252
- 2019Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systemscited by 215
- 2020MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselinescited by 201
- 2016Neural Belief Tracker: Data-Driven Dialogue State Trackingcited by 142
- 2020TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Trackingcited by 130
- 2020A Survey of Label-noise Representation Learning: Past, Present and Futurecited by 121
- 2017Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systemscited by 84
12 more
- 2019Efficient Dialogue State Tracking by Selectively Overwriting Memorycited by 76
- 2019Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Trackingcited by 75
- 2020CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Datasetcited by 66
- 2019SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Trackingcited by 63
- 2020MultiWOZ 2.3: A multi-domain task-oriented dialogue dataset enhanced with annotation corrections and co-reference annotationcited by 51
- 2018Global-Locally Self-Attentive Dialogue State Trackercited by 45
- 2020CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackerscited by 43
- 2020RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modelingcited by 40
- 2020Conversational Semantic Parsing for Dialog State Trackingcited by 40
- 2021Slot Self-Attentive Dialogue State Trackingcited by 27
- 2020A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Trackingcited by 20
- 2020Parallel Interactive Networks for Multi-Domain Dialogue State Generationcited by 6
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Citing papers per year: 2021: 7; 2022: 18; 2023: 16; 2024: 18; 2025: 15; 2026: 12.
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