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

Published: 27 Mar 2022, Last Modified: 05 May 2023FL4NLP@ACL2022Readers: Everyone
Keywords: Personalization, sentiment analysis
TL;DR: This approach produces personalized classification responses by prepending a fixed, randomly generated, user-specific string (called ``user identifier'') to each user's input text.
Abstract: Globally federated models are trained to be as generalizable as possible, with user invariance considered desirable since the models are shared across multitudes of users. As such, these models are often unable to produce personalized responses for individual users, based on their data. Contrary to widely-used personalization techniques based on meta and few-shot learning, we propose \uid, a novel scheme for training a single shared model for all users. Our approach produces personalized responses by adding fixed, non-trainable user identifiers to the input data. We empirically demonstrate that this proposed method outperforms the prefix-tuning based state-of-the-art approach by up to $13\%$, on a suite of sentiment analysis datasets. We also show that, unlike prior work, this method needs neither any additional model parameters nor any extra rounds of few-shot fine-tuning.
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