Leveraging Similar Users for Personalized Language Modeling with Limited Data
Charles Welch, Chenxi Gu, Jonathan K. Kummerfeld, Veronica Perez-Rosas, Rada Mihalcea
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.
BibTeX
@inproceedings{welch-etal-2022-leveraging,
title = "Leveraging Similar Users for Personalized Language Modeling with Limited Data",
author = "Welch, Charles and
Gu, Chenxi and
Kummerfeld, Jonathan K. and
Perez-Rosas, Veronica and
Mihalcea, Rada",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.122/",
doi = "10.18653/v1/2022.acl-long.122",
pages = "1742--1752"
}