Searching for Snippets of Open-Domain Dialogue in Task-Oriented Dialogue Datasets
This work addresses the gap in understanding how humans blend chit-chat into task-oriented conversations, though it is incremental as it primarily identifies existing data rather than creating new methods or models.
The study investigated whether task-oriented dialogue datasets naturally contain open-domain chit-chat sequences by analyzing Schema-Guided Dialogues and MultiWOZ using topic modeling and keyword similarity, finding that such sequences are indeed present.
Most existing dialogue corpora and models have been designed to fit into 2 predominant categories : task-oriented dialogues portray functional goals, such as making a restaurant reservation or booking a plane ticket, while chit-chat/open-domain dialogues focus on holding a socially engaging talk with a user. However, humans tend to seamlessly switch between modes and even use chitchat to enhance task-oriented conversations. To bridge this gap, new datasets have recently been created, blending both communication modes into conversation examples. The approaches used tend to rely on adding chit-chat snippets to pre-existing, human-generated task-oriented datasets. Given the tendencies observed in humans, we wonder however if the latter do not \textit{already} hold chit-chat sequences. By using topic modeling and searching for topics which are most similar to a set of keywords related to social talk, we explore the training sets of Schema-Guided Dialogues and MultiWOZ. Our study shows that sequences related to social talk are indeed naturally present, motivating further research on ways chitchat is combined into task-oriented dialogues.