MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Confidence Estimation for LLM-Based Dialogue State Tracking
Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models
JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset
Exploring the Viability of Synthetic Audio Data for Audio-Based Dialogue State Tracking
Turn-Level Active Learning for Dialogue State Tracking
Towards LLM-driven Dialogue State Tracking
토론
이 논문과 연결된 포럼 글이 아직 없습니다.