On learning and representing social meaning in NLP: a sociolinguistic perspective

Dong Nguyen; Laura Rosseel; Jack Grieve

doi:10.18653/v1/2021.naacl-main.50

On learning and representing social meaning in NLP: a sociolinguistic perspective

Dong Nguyen
, Laura Rosseel
, Jack Grieve

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

Abstract

The field of NLP has made substantial progress in building meaning representations. However, an important aspect of linguistic meaning, social meaning, has been largely overlooked. We introduce the concept of social meaning to NLP and discuss how insights from sociolinguistics can inform work on representation learning in NLP. We also identify key challenges for this new line of research.

Original language	English
Title of host publication	Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Editors	Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Publisher	Association for Computational Linguistics
Pages	603-612
Number of pages	10
DOIs	https://doi.org/10.18653/v1/2021.naacl-main.50
Publication status	Published - Jun 2021

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2021.naacl-main.50Final published version, 266 KBLicence: CC BY

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Nguyen, D., Rosseel, L., & Grieve, J. (2021). On learning and representing social meaning in NLP: a sociolinguistic perspective. In K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, & Y. Zhou (Eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 603-612). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.50

Nguyen, Dong ; Rosseel, Laura ; Grieve, Jack. / On learning and representing social meaning in NLP: a sociolinguistic perspective. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. editor / Kristina Toutanova ; Anna Rumshisky ; Luke Zettlemoyer ; Dilek Hakkani-Tur ; Iz Beltagy ; Steven Bethard ; Ryan Cotterell ; Tanmoy Chakraborty ; Yichao Zhou. Association for Computational Linguistics, 2021. pp. 603-612

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title = "On learning and representing social meaning in NLP: a sociolinguistic perspective",

abstract = "The field of NLP has made substantial progress in building meaning representations. However, an important aspect of linguistic meaning, social meaning, has been largely overlooked. We introduce the concept of social meaning to NLP and discuss how insights from sociolinguistics can inform work on representation learning in NLP. We also identify key challenges for this new line of research.",

author = "Dong Nguyen and Laura Rosseel and Jack Grieve",

year = "2021",

month = jun,

doi = "10.18653/v1/2021.naacl-main.50",

language = "English",

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editor = "Kristina Toutanova and Anna Rumshisky and Luke Zettlemoyer and Dilek Hakkani-Tur and Iz Beltagy and Steven Bethard and Ryan Cotterell and Tanmoy Chakraborty and Yichao Zhou",

booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",

publisher = "Association for Computational Linguistics",

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Nguyen, D, Rosseel, L & Grieve, J 2021, On learning and representing social meaning in NLP: a sociolinguistic perspective. in K Toutanova, A Rumshisky, L Zettlemoyer, D Hakkani-Tur, I Beltagy, S Bethard, R Cotterell, T Chakraborty & Y Zhou (eds), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, pp. 603-612. https://doi.org/10.18653/v1/2021.naacl-main.50

On learning and representing social meaning in NLP: a sociolinguistic perspective. / Nguyen, Dong; Rosseel, Laura; Grieve, Jack.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. ed. / Kristina Toutanova; Anna Rumshisky; Luke Zettlemoyer; Dilek Hakkani-Tur; Iz Beltagy; Steven Bethard; Ryan Cotterell; Tanmoy Chakraborty; Yichao Zhou. Association for Computational Linguistics, 2021. p. 603-612.

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

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AB - The field of NLP has made substantial progress in building meaning representations. However, an important aspect of linguistic meaning, social meaning, has been largely overlooked. We introduce the concept of social meaning to NLP and discuss how insights from sociolinguistics can inform work on representation learning in NLP. We also identify key challenges for this new line of research.

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Nguyen D, Rosseel L, Grieve J. On learning and representing social meaning in NLP: a sociolinguistic perspective. In Toutanova K, Rumshisky A, Zettlemoyer L, Hakkani-Tur D, Beltagy I, Bethard S, Cotterell R, Chakraborty T, Zhou Y, editors, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics. 2021. p. 603-612 doi: 10.18653/v1/2021.naacl-main.50

On learning and representing social meaning in NLP: a sociolinguistic perspective

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