Returning the N to NLP: Towards Contextually Personalized Classification Models

By Lucie Flekova. 2020

76 citations recorded by the source corpus.

Most NLP models today treat language as universal, even though socio- and psycholingustic research shows that the communicated message is influenced by the characteristics of the speaker as well as the target audience. This paper surveys the landscape of personalization in natural language processing and related fields, and offers a path forward to mitigate the decades of deviation of the NLP tools from sociolingustic findings, allowing to flexibly process the “natural” language of each user rather than enforcing a uniform NLP treatment. It outlines a possible direction to incorporate these aspects into neural NLP models by means of socially contextual personalization, and proposes to shift the focus of our evaluation strategies accordingly.

Subfields: Artificial Intelligence, Natural Language Processing

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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).”
Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning (2021, methodology): “emplify the promising yet underexplored direction of user context modeling (Flek, 2020) for suicide ideation detection.”
On learning and representing social meaning in NLP: a sociolinguistic perspective (2021, background): “Our paper also complements the discussion by Flek (2020).”
A survey on sentiment analysis methods, applications, and challenges (2022, background): “(Fredriksen-Goldsen and Kim 2017) Additionally, the term chosen for expression represents the demographic characteristics of individuals, such as gender and age, and its desire, social standing, and personality, other psychological and social characteristics (Flek 2020).”
Five sources of bias in natural language processing (2021, background): “And language carries a lot of secondary information about the speaker, their self-identification, and membership in socio-demographic groups (Flek, 2020; Hovy & Spruit, 2016; Hovy & Yang, 2021).”
The Importance of Modeling Social Factors of Language: Theory and Practice (2021, background): “…reaching a point where this idea can become implemented in systems, it is a message that bears repeating in the NLP community (see also Hovy (2018) and Flek (2020) for similar points, as well as Nguyen et al. (2016) for an overview of the closely related issue of computational sociolinguistics).”