Multiwoz 2.4: A Multi-domain Task-oriented Dialogue Dataset With Essential Annotation Corrections To Improve State Tracking Evaluation | Awesome LLM Papers Contribute to Awesome LLM Papers

Awesome LLM Papers

A curated collection of research papers on Large Language Models (LLMs), transformers, prompting, fine-tuning, and multi-modal systems. Maintained by Sean Moran.

Contact Sean Moran about this survey or website. Made with Jekyll and Hyde.

Multiwoz 2.4: A Multi-domain Task-oriented Dialogue Dataset With Essential Annotation Corrections To Improve State Tracking Evaluation

Fanghua Ye, Jarana Manotumruksa, Emine Yilmaz . Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue 2022 – 55 citations

[Paper]   Search on Google Scholar   Search on Semantic Scholar
Datasets Dialogue & Multi Turn Evaluation Tools Training Techniques

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.

Similar Work