2020/10/24 by Kai Sun, Seungwhan Moon, Sun, Kai +17 · 10 citations
Computer Science · #AI in Service Interactions #Annotation #Artificial intelligence #Computation and Language (cs.CL) #Computer science #FOS: Computer and information sciences #Human–computer interaction #Information retrieval #Multimedia #Schema (genetic algorithms) #Speech and dialogue systems #Task (project management) #Topic Modeling #World Wide Web #cs.CL
paper · pdf · doi:10.48550/arxiv.2010.12757
published in arXiv (Cornell University) (Cornell University) · To appear in NAACL-HLT 2021
openalex publication_date 2020/10/24 · arxiv created 2021/05/01 · arxiv updated 2021/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.