UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis

Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis


Abstract
Global models are typically trained to be as generalizable as possible. Invariance to the specific user is considered desirable since models are shared across multitudes of users. However, these models are often unable to produce personalized responses for individual users, based on their data. Contrary to widely-used personalization techniques based on few-shot and meta-learning, we propose UserIdentifier, a novel scheme for training a single shared model for all users. Our approach produces personalized responses by prepending a fixed, user-specific non-trainable string (called “user identifier”) to each user’s input text. Unlike prior work, this method doesn’t need any additional model parameters, any extra rounds of personal few-shot learning or any change made to the vocabulary. We empirically study different types of user identifiers (numeric, alphanumeric, and also randomly generated) and demonstrate that, surprisingly, randomly generated user identifiers outperform the prefix-tuning based state-of-the-art approach by up to 13, on a suite of sentiment analysis datasets.
Anthology ID:
2022.naacl-main.252
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3449–3456
Language:
URL:
https://aclanthology.org/2022.naacl-main.252
DOI:
10.18653/v1/2022.naacl-main.252
Bibkey:
Cite (ACL):
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, and Dimitrios Dimitriadis. 2022. UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3449–3456, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis (Mireshghallah et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.252.pdf