Intent Induction from Conversations for Task-Oriented Dialogue Track at DSTC 11

• Main Authors: Gung, James, Shu, Raphael, Moeng, Emily, Rose, Wesley, Romeo, Salvatore, Benajiba, Yassine, Gupta, Arshit, Mansour, Saab, Zhang, Yi
• Format: Journal Article
• Language: English
• Published: 25.04.2023
• Subjects: Computer Science - Computation and Language
• DOI: 10.48550/arxiv.2304.12982

With increasing demand for and adoption of virtual assistants, recent work has investigated ways to accelerate bot schema design through the automatic induction of intents or the induction of slots and dialogue states. However, a lack of dedicated benchmarks and standardized evaluation has made progress difficult to track and comparisons between systems difficult to make. This challenge track, held as part of the Eleventh Dialog Systems Technology Challenge, introduces a benchmark that aims to evaluate methods for the automatic induction of customer intents in a realistic setting of customer service interactions between human agents and customers. We propose two subtasks for progressively tackling the automatic induction of intents and corresponding evaluation methodologies. We then present three datasets suitable for evaluating the tasks and propose simple baselines. Finally, we summarize the submissions and results of the challenge track, for which we received submissions from 34 teams.