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

Dong Nguyen; Laura Rosseel; Jack Grieve

NAACL 2021long42 citations

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.

BibTeX

@inproceedings{nguyen-etal-2021-learning,
    title = "On learning and representing social meaning in {NLP}: a sociolinguistic perspective",
    author = "Nguyen, Dong  and
      Rosseel, Laura  and
      Grieve, Jack",
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-main.50/",
    doi = "10.18653/v1/2021.naacl-main.50",
    pages = "603--612"
}