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.

翻译：以往关于以任务为导向的对话系统的多数工作仅限于有限的域内API范围,而用户往往有与域内要求有关但API没有涵盖的域内要求,这一挑战轨道旨在通过纳入外部非结构化知识来源,扩大面向任务的对话系统的覆盖面。我们界定了三项任务:寻求知识的转弯检测、知识选择和基于知识的响应生成。我们为三项任务引入数据集和神经基线模型。挑战轨道共从24个参与小组获得105个条目。在评价结果中,不同大规模预先培训的语言模式的组合方法在提高知识选择能力、更好地将数据概括化为不可见数据的情况下取得了高性能。