Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.18653/v1/2022.acl-long.122 | PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS) |
DocType | Volume | Citations |
Conference | Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Charles Welch | 1 | 0 | 0.34 |
| Chenxi Gu | 2 | 0 | 0.34 |
| Jonathan K. Kummerfeld | 3 | 93 | 16.19 |
| Verónica Pérez-Rosas | 4 | 40 | 5.02 |
| Rada Mihalcea | 5 | 6460 | 445.54 |