GrounDialog: A Dataset for Repair and Grounding in Task-oriented Spoken Dialogues for Language Learning


Improving conversational proficiency is a key target for students learning a new language. While acquiring conversational proficiency, students must learn the linguistic mechanisms of Repair and Grounding (R&G) to negotiate meaning and find common ground with their interlocutor so conversational breakdowns can be resolved. Task-oriented Spoken Dialogue Systems (SDS) have long been sought as a tool to hone conversational proficiency. However, the R&G patterns for language learners interacting with a task-oriented spoken dialogue system are not reflected explicitly in any existing datasets. Therefore, to move the needle in Spoken Dialogue Systems for language learning we present GrounDialog: an annotated dataset of spoken conversations where we elicit a rich set of R&G patterns.

Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications
Xuanming Zhang
Xuanming Zhang
PhD student in Computer Science

My research interests include Natural Language Processing, Social Computing, Dialogue Systems and Human-Computer Interaction.