Most prior work on task-oriented dialogue systems are restricted to a limited
coverage of domain APIs, while users oftentimes have domain related requests
that are not covered by the APIs. This challenge track aims to expand the
coverage of task-oriented dialogue systems by incorporating external
unstructured knowledge sources. We define three tasks: knowledge-seeking turn
detection, knowledge selection, and knowledge-grounded response generation. We
introduce the data sets and the neural baseline models for three tasks. The
challenge track received a total of 105 entries from 24 participating teams. In
the evaluation results, the ensemble methods with different large-scale
pretrained language models achieved high performances with improved knowledge
selection capability and better generalization into unseen data.

任务驱动的对话系统使用外部非结构化知识源来扩展任务领域 API 的覆盖范围，三个任务包括知识寻找、选择和响应生成，并用集成方法和大规模预训练语言模型取得了高性能。