ViWOZ: A Multi-Domain Task-Oriented Dialogue Systems Dataset For Low-resource Language
원문 초록
Most of the current task-oriented dialogue systems (ToD), despite having interesting results, are designed for a handful of languages like Chinese and English. Therefore, their performance in low-resource languages is still a significant problem due to the absence of a standard dataset and evaluation policy. To address this problem, we proposed ViWOZ, a fully-annotated Vietnamese task-oriented dialogue dataset. ViWOZ is the first multi-turn, multi-domain tasked oriented dataset in Vietnamese, a low-resource language. The dataset consists of a total of 5,000 dialogues, including 60,946 fully annotated utterances. Furthermore, we provide a comprehensive benchmark of both modular and end-to-end models in low-resource language scenarios. With those characteristics, the ViWOZ dataset enables future studies on creating a multilingual task-oriented dialogue system.
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WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue
EnSToM: Enhancing Dialogue Systems with Entropy-Scaled Steering Vectors for Topic Maintenance
LANID: LLM-assisted New Intent Discovery
Leveraging Graph Structures and Large Language Models for End-to-End Synthetic Task-Oriented Dialogues
Pseudo-Label Enhanced Prototypical Contrastive Learning for Uniformed Intent Discovery
Confidence Estimation for LLM-Based Dialogue State Tracking
토론
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