@inproceedings{markowska-etal-2023-finding,
title = "Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations",
author = "Markowska, Magdalena and
Taghizadeh, Mohammad and
Soubki, Adil and
Mirroshandel, Seyed and
Rambow, Owen",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.551",
doi = "10.18653/v1/2023.findings-emnlp.551",
pages = "8221--8233",
abstract = "When we communicate with other humans, we do not simply generate a sequence of words. Rather, we use our cognitive state (beliefs, desires, intentions) and our model of the audience{'}s cognitive state to create utterances that affect the audience{'}s cognitive state in the intended manner. An important part of cognitive state is the common ground, which is the content the speaker believes, and the speaker believes the audience believes, and so on. While much attention has been paid to common ground in cognitive science, there has not been much work in natural language processing. In this paper, we introduce a new annotation and corpus to capture common ground. We then describe some initial experiments extracting propositions from dialog and tracking their status in the common ground from the perspective of each speaker.",
}
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<abstract>When we communicate with other humans, we do not simply generate a sequence of words. Rather, we use our cognitive state (beliefs, desires, intentions) and our model of the audience’s cognitive state to create utterances that affect the audience’s cognitive state in the intended manner. An important part of cognitive state is the common ground, which is the content the speaker believes, and the speaker believes the audience believes, and so on. While much attention has been paid to common ground in cognitive science, there has not been much work in natural language processing. In this paper, we introduce a new annotation and corpus to capture common ground. We then describe some initial experiments extracting propositions from dialog and tracking their status in the common ground from the perspective of each speaker.</abstract>
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%0 Conference Proceedings
%T Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations
%A Markowska, Magdalena
%A Taghizadeh, Mohammad
%A Soubki, Adil
%A Mirroshandel, Seyed
%A Rambow, Owen
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F markowska-etal-2023-finding
%X When we communicate with other humans, we do not simply generate a sequence of words. Rather, we use our cognitive state (beliefs, desires, intentions) and our model of the audience’s cognitive state to create utterances that affect the audience’s cognitive state in the intended manner. An important part of cognitive state is the common ground, which is the content the speaker believes, and the speaker believes the audience believes, and so on. While much attention has been paid to common ground in cognitive science, there has not been much work in natural language processing. In this paper, we introduce a new annotation and corpus to capture common ground. We then describe some initial experiments extracting propositions from dialog and tracking their status in the common ground from the perspective of each speaker.
%R 10.18653/v1/2023.findings-emnlp.551
%U https://aclanthology.org/2023.findings-emnlp.551
%U https://doi.org/10.18653/v1/2023.findings-emnlp.551
%P 8221-8233
Markdown (Informal)
[Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations](https://aclanthology.org/2023.findings-emnlp.551) (Markowska et al., Findings 2023)
ACL