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.
@inproceedings{zhang-etal-2023-groundialog, title = "{G}roun{D}ialog: A Dataset for Repair and Grounding in Task-oriented Spoken Dialogues for Language Learning", author = "Zhang, Xuanming and Divekar, Rahul and Ubale, Rutuja and Yu, Zhou", editor = {Kochmar, Ekaterina and Burstein, Jill and Horbach, Andrea and Laarmann-Quante, Ronja and Madnani, Nitin and Tack, Ana{\"i}s and Yaneva, Victoria and Yuan, Zheng and Zesch, Torsten}, booktitle = "Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.bea-1.26/", doi = "10.18653/v1/2023.bea-1.26", pages = "300--314", abstract = "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{\textbackslash}{\&}amp;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{\&}amp;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{\&}amp;G patterns."}
%0 Conference Proceedings%T GrounDialog: A Dataset for Repair and Grounding in Task-oriented Spoken Dialogues for Language Learning%A Zhang, Xuanming%A Divekar, Rahul%A Ubale, Rutuja%A Yu, Zhou%Y Kochmar, Ekaterina%Y Burstein, Jill%Y Horbach, Andrea%Y Laarmann-Quante, Ronja%Y Madnani, Nitin%Y Tack, Anaïs%Y Yaneva, Victoria%Y Yuan, Zheng%Y Zesch, Torsten%S Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)%D 2023%8 July%I Association for Computational Linguistics%C Toronto, Canada%F zhang-etal-2023-groundialog%X 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\textbackslash&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.%R 10.18653/v1/2023.bea-1.26%U https://aclanthology.org/2023.bea-1.26/%U https://doi.org/10.18653/v1/2023.bea-1.26%P 300-314
[GrounDialog: A Dataset for Repair and Grounding in Task-oriented Spoken Dialogues for Language Learning](https://aclanthology.org/2023.bea-1.26/) (Zhang et al., BEA 2023)