Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations

Magdalena Markowska; Mohammad Taghizadeh; Adil Soubki; Seyed Abolghasem Mirroshandel; Owen Rambow

doi:10.18653/v1/2023.findings-emnlp.551

Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations

Magdalena Markowska
, Mohammad Taghizadeh
, Adil Soubki
, Seyed Abolghasem Mirroshandel
, Owen Rambow

Linguistics

Institute for Advanced Computational Science
Stony Brook University
Guilan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

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.

Original language	English
Title of host publication	Findings of the Association for Computational Linguistics
Subtitle of host publication	EMNLP 2023
Publisher	Association for Computational Linguistics (ACL)
Pages	8221-8233
Number of pages	13
ISBN (Electronic)	9798891760615
DOIs	https://doi.org/10.18653/v1/2023.findings-emnlp.551
State	Published - 2023
Event	2023 Findings of the Association for Computational Linguistics: EMNLP 2023 - Hybrid, Singapore Duration: Dec 6 2023 → Dec 10 2023

Publication series

Name	Findings of the Association for Computational Linguistics: EMNLP 2023

Conference

Conference	2023 Findings of the Association for Computational Linguistics: EMNLP 2023
Country/Territory	Singapore
City	Hybrid
Period	12/6/23 → 12/10/23

Access to Document

10.18653/v1/2023.findings-emnlp.551

Cite this

Markowska, M., Taghizadeh, M., Soubki, A., Mirroshandel, S. A., & Rambow, O. (2023). Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 8221-8233). (Findings of the Association for Computational Linguistics: EMNLP 2023). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.findings-emnlp.551

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title = "Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations",

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.",

author = "Magdalena Markowska and Mohammad Taghizadeh and Adil Soubki and Mirroshandel, \{Seyed Abolghasem\} and Owen Rambow",

note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 2023 Findings of the Association for Computational Linguistics: EMNLP 2023 ; Conference date: 06-12-2023 Through 10-12-2023",

year = "2023",

doi = "10.18653/v1/2023.findings-emnlp.551",

language = "English",

series = "Findings of the Association for Computational Linguistics: EMNLP 2023",

publisher = "Association for Computational Linguistics (ACL)",

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Markowska, M, Taghizadeh, M, Soubki, A, Mirroshandel, SA & Rambow, O 2023, Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations. in Findings of the Association for Computational Linguistics: EMNLP 2023. Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics (ACL), pp. 8221-8233, 2023 Findings of the Association for Computational Linguistics: EMNLP 2023, Hybrid, Singapore, 12/6/23. https://doi.org/10.18653/v1/2023.findings-emnlp.551

Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations. / Markowska, Magdalena; Taghizadeh, Mohammad; Soubki, Adil et al.
Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL), 2023. p. 8221-8233 (Findings of the Association for Computational Linguistics: EMNLP 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Rambow, Owen

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AB - 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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Markowska M, Taghizadeh M, Soubki A, Mirroshandel SA, Rambow O. Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations. In Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL). 2023. p. 8221-8233. (Findings of the Association for Computational Linguistics: EMNLP 2023). doi: 10.18653/v1/2023.findings-emnlp.551

Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations

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