%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