Existing studies in dialogue system research mostly treat task-oriented
dialogue and chit-chat as separate domains. Towards building a human-like
assistant that can converse naturally and seamlessly with users, it is
important to build a dialogue system that conducts both types of conversations
effectively. In this work, we investigate how task-oriented dialogue and
knowledge-grounded chit-chat can be effectively integrated into a single model.
To this end, we create a new dataset, KETOD (Knowledge-Enriched Task-Oriented
Dialogue), where we naturally enrich task-oriented dialogues with chit-chat
based on relevant entity knowledge. We also propose two new models,
SimpleToDPlus and Combiner, for the proposed task. Experimental results on both
automatic and human evaluations show that the proposed methods can
significantly improve the performance in knowledge-enriched response generation
while maintaining a competitive task-oriented dialog performance. We believe
our new dataset will be a valuable resource for future studies. Our dataset and
code are publicly available at https://github.com/facebookresearch/ketod.

在这项工作中，我们研究了如何将任务定向对话和有知识的聊天有机地整合到单个模型中，通过创建一个新的数据集 KETOD 来自然地丰富基于相关实体知识的任务定向对话。我们还提出了两个新模型，SimpleToDPlus 和 Combiner，在自动和人工评估的实验结果中表现出显著的优越性能。