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 \url{https://github.com/facebookresearch/ketod}.
翻译:对话系统研究的现有研究大多将任务导向的对话和聊天作为独立的领域。为了建立一个能够自然地和无缝地与用户进行交流的像人一样的助理,重要的是要建立一个能够有效进行两种对话的对话系统。在这项工作中,我们调查如何有效地将任务导向的对话和基于知识的奇特聊天纳入单一模式。为此,我们建立了一个新的数据集,即KETOD(知识丰富的任务导向对话),我们在此自然地丰富基于相关实体知识与chit-chat进行的任务导向对话的内容。我们还为拟议的任务提出了两种新模式,即“简单托普”和“组合器”。关于自动和人类评价的实验结果显示,拟议的方法可以大大改进知识丰富反应生成的绩效,同时保持竞争性的任务导向对话性能。我们相信,我们的新数据集将成为今后研究的宝贵资源。我们的数据集和代码可在以下网站公开查阅:<url{https://github.com/facebookreear/ketod}。