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.- Anthology ID:
- 2022.acl-long.122
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1742–1752
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.122
- DOI:
- 10.18653/v1/2022.acl-long.122
- Cite (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.
- Cite (Informal):
- Leveraging Similar Users for Personalized Language Modeling with Limited Data (Welch et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.acl-long.122.pdf