On learning and representing social meaning in NLP: a sociolinguistic perspective
Publication date
2021-06
Authors
Nguyen, Dong
Rosseel, Laura
Grieve, Jack
Editors
Toutanova, Kristina
Rumshisky, Anna
Zettlemoyer, Luke
Hakkani-Tur, Dilek
Beltagy, Iz
Bethard, Steven
Cotterell, Ryan
Chakraborty, Tanmoy
Zhou, Yichao
Advisors
Supervisors
Document Type
Part of book
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License
cc_by
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.
Keywords
Citation
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