@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.",
}
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<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.</abstract>
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%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
Markdown (Informal)
[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)
ACL