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

By Dong Nguyen, Laura Rosseel, Jack Grieve. 2021

44 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Noah A. Smith
2. Y-Lan Boureau
3. Yejin Choi
4. Jason Aaron Edward Weston
5. Emily Dinan
6. Dirk Hovy
7. Luke S. Zettlemoyer
8. Mohit Bansal

In Experimental and Cognitive Psychology

1. Noah A. Smith
2. Verena Rieser
3. Gavin Abercrombie
4. Luke S. Zettlemoyer
5. Dirk Hovy
6. Zeerak Talat

In Natural Language Processing

1. Y-Lan Boureau
2. Noah A. Smith
3. Jason Aaron Edward Weston
4. Emily Dinan
5. Yejin Choi
6. Dirk Hovy
7. Luke S. Zettlemoyer
8. Verena Rieser

Citations in the corpus

• Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “Some of the issues detailed in this paper may be attributed to a lack of language understanding, especially the social meaning of language (Hovy & Spruit, 2016; Flek, 2020; Hovy & Yang, 2021; Nguyen et al., 2021).”
• Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection (2022, background): “, 2021), which tend to strip documents of pragmatic nuance (Baldwin and Chai, 2011) and social signals (Nguyen et al., 2021).”