Returning the N to NLP: Towards Contextually Personalized Classification Models

Abstract

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.

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

“…Our paper also complements the discussion by Flek (2020). Recognising that language variation is inherent to language, Flek (2020) argues for personalised NLP systems to improve language understanding.…”

Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning

“…Deep learning models like CNNs (Naderi et al, 2019) and LSTMs (Coppersmith et al, 2018) have improved suicide ideation detection (Ji et al, 2020) thanks to a more robust semantic context to interpret the tweet in question, however, lacking user-level context, are often unable to ascertain suicide risk (Sisask et al, 2008). The best performing models (Matero et al, 2019;Naderi et al, 2019) at the CLPsych (Zirikly et al, 2019) and CLEF e-Risk (Losada et al, 2019) exemplify the promising yet underexplored direction of user context modeling (Flek, 2020) for suicide ideation detection. Although recent studies (Shing et al, 2020;Sawhney et al, 2020) explore the personal historical context of users, community-based social context has rarely been explored for this task.…”

“…We note that user-contextual models drastically outperform RF+TF and LSTM that only leverage the language of the tweet without any additional user context. We attribute these improvements to the ability of personally contextual models to better ascertain a user's mental state through their historical activity and communities they interact with (Flek, 2020). (Joiner, 2007(Joiner, , 2009Orden et al, 2010).…”

A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media

“…Shared tasks such as CLPsych (Zirikly et al, 2019) andCLEF eRISK (Losada et al, 2020) have seen a rise in neural networks such as CNNs (Yates et al, 2017;Du et al, 2018;Naderi et al, 2019;Gaur et al, 2019) and LSTMs (Ji et al, 2018;Tadesse et al, 2020) to predict suicide risk. While these methods capture post semantics in isolation, no user context is leveraged, hindering insight into the user's mental state to improve predictive power (Venek et al, 2017;Flek, 2020). User context includes the user's emotions (Ren et al, 2016;Guntuku et al, 2017), social networks and historical posts (Mathur et al, 2020).…”

“…Figure 1 illustrates how features such as historical posts (Matero et al, 2019) can add context for analyzing a user's online behavior over time (Van Heeringen and Marušic, 2003) to better ascertain suicide risk. Despite the success of usercentric contextual models (Flek, 2020) for suicide ideation detection, they have two major limitations.…”