Papers with MultiWOZ 2.0 dataset
An Adaptive Prompt Generation Framework for Task-oriented Dialogue System (2023.findings-emnlp)
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| Challenge: | Existing black-box large language models (LLMs) have excellent performance in task-oriented dialogue (TOD) tasks, but obtaining suitable prompts for specific tasks is challenging. |
| Approach: | They propose a black-box large language model that generates domain and slot information in the belief state, which serves as prior knowledge for subsequent prompt generation. |
| Outcome: | The proposed framework outperforms existing prompting methods on the MultiWOZ 2.0 dataset. |
ASSIST: Towards Label Noise-Robust Dialogue State Tracking (2022.findings-acl)
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| Challenge: | Existing versions of MultiWOZ 2.0 have been published, but there are still lots of noisy labels in the training set. |
| Approach: | They propose a framework to train dialogue state tracking models from noisy labels instead of improving annotation quality further by using auxiliary models. |
| Outcome: | The proposed framework improves the goal accuracy of DST models by 28.16% on MultiWOZ 2.0 and 8.41% on MultiWoz 2.4, compared to using only the vanilla noisy labels. |
Modeling Long Context for Task-Oriented Dialogue State Generation (2020.acl-main)
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| Challenge: | Existing approaches to dialogue state tracking are limited to scenarios with infinite slot values and prediction of unseen slot values. |
| Approach: | They propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model as an auxiliary task for task-oriented dialogue state generation. |
| Outcome: | The proposed model achieves state-of-the-art accuracy on the MultiWOZ 2.0 dataset. |