Chenxi Gu


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Leveraging Similar Users for Personalized Language Modeling with Limited Data
Charles Welch | Chenxi Gu | Jonathan K. Kummerfeld | Veronica Perez-Rosas | Rada Mihalcea
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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.