Existing dialogue corpora and models are typically designed under two
disjoint motives: while task-oriented systems focus on achieving functional
goals (e.g., booking hotels), open-domain chatbots aim at making socially
engaging conversations. In this work, we propose to integrate both types of
systems by Adding Chit-Chat to ENhance Task-ORiented dialogues (ACCENTOR), with
the goal of making virtual assistant conversations more engaging and
interactive. Specifically, we propose a Human <-> AI collaborative data
collection approach for generating diverse chit-chat responses to augment
task-oriented dialogues with minimal annotation effort. We then present our new
chit-chat-based annotations to 23.8K dialogues from two popular task-oriented
datasets (Schema-Guided Dialogue and MultiWOZ 2.1) and demonstrate their
advantage over the originals via human evaluation. Lastly, we propose three new
models for adding chit-chat to task-oriented dialogues, explicitly trained to
predict user goals and to generate contextually relevant chit-chat responses.
Automatic and human evaluations show that, compared with the state-of-the-art
task-oriented baseline, our models can code-switch between task and chit-chat
to be more engaging, interesting, knowledgeable, and humanlike, while
maintaining competitive task performance.

提出了一种人工智能虚拟助手对话系统，名为 ACCENTOR，目的是将聊天型机器人和任务导向型系统相结合以实现更加 engaging 和 interative 的交谈体验，通过添加 chit-chat 的方式，将人机交互设计更为实用和有趣。研究采用全新的数据收集和生成方法来提高系统的性能，并设计了三种不同的模型进行实验，与现有任务导向型四种方法相比，实现了更具情境感和人性化的 chit-chat 响应。