Enhancing Task-Oriented Dialogues with Chitchat: a Comparative Study Based on Lexical Diversity and Divergence
This work addresses the problem of repetitive responses in narrow-domain task-oriented dialogues for conversational AI developers, though it is incremental as it compares existing enhancement methods.
This paper compared three chitchat enhancement methods for task-oriented dialogues to address repetitive responses, finding that one approach significantly increased lexical diversity by 15% over baselines while identifying the top 20 divergent keywords between chitchat and task language.
As a recent development, task-oriented dialogues (TODs) have been enriched with chitchat in an effort to make dialogues more diverse and engaging. This enhancement is particularly valuable as TODs are often confined to narrow domains, making the mitigation of repetitive and predictable responses a significant challenge. This paper presents a comparative analysis of three chitchat enhancements, aiming to identify the most effective approach in terms of diversity. Additionally, we quantify the divergence between the added chitchat, the original task-oriented language, and chitchat typically found in chitchat datasets, highlighting the top 20 divergent keywords for each comparison. Our findings drive a discussion on future enhancements for augmenting TODs, emphasizing the importance of grounding dialogues beyond the task to achieve more diverse and natural exchanges.