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        <title>Leveraging Similar Users for Personalized Language Modeling with Limited Data</title>
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    <abstract>Personalized language models are designed and trained to capture language patterns specific to individual users. This makes them more accurate at predicting what a user will write. However, when a new user joins a platform and not enough text is available, it is harder to build effective personalized language models. We propose a solution for this problem, using a model trained on users that are similar to a new user. In this paper, we explore strategies for finding the similarity between new users and existing ones and methods for using the data from existing users who are a good match. We further explore the trade-off between available data for new users and how well their language can be modeled.</abstract>
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%0 Conference Proceedings
%T Leveraging Similar Users for Personalized Language Modeling with Limited Data
%A Welch, Charles
%A Gu, Chenxi
%A Kummerfeld, Jonathan K.
%A Perez-Rosas, Veronica
%A Mihalcea, Rada
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F welch-etal-2022-leveraging
%X Personalized language models are designed and trained to capture language patterns specific to individual users. This makes them more accurate at predicting what a user will write. However, when a new user joins a platform and not enough text is available, it is harder to build effective personalized language models. We propose a solution for this problem, using a model trained on users that are similar to a new user. In this paper, we explore strategies for finding the similarity between new users and existing ones and methods for using the data from existing users who are a good match. We further explore the trade-off between available data for new users and how well their language can be modeled.
%R 10.18653/v1/2022.acl-long.122
%U https://aclanthology.org/2022.acl-long.122
%U https://doi.org/10.18653/v1/2022.acl-long.122
%P 1742-1752

Markdown (Informal)

[Leveraging Similar Users for Personalized Language Modeling with Limited Data](https://aclanthology.org/2022.acl-long.122) (Welch et al., ACL 2022)

• Leveraging Similar Users for Personalized Language Modeling with Limited Data (Welch et al., ACL 2022)

ACL

• Charles Welch, Chenxi Gu, Jonathan K. Kummerfeld, Veronica Perez-Rosas, and Rada Mihalcea. 2022. Leveraging Similar Users for Personalized Language Modeling with Limited Data. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1742–1752, Dublin, Ireland. Association for Computational Linguistics.