@inproceedings{c78afe2d84d14de0bc2898bb2330735b,
title = "User factor adaptation for user embedding via multitask learning",
abstract = "Language varies across users and their interested fields in social media data: words authored by a user across his/her interests may have different meanings (e.g., cool) or sentiments (e.g., fast). However, most of the existing methods to train user embeddings ignore the variations across user interests, such as product and movie categories (e.g., drama vs. action). In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets. We then propose a user embedding model to account for the language variability of user interests via a multitask learning framework. The model learns user language and its variations without human supervision. While existing work mainly evaluated the user embedding by extrinsic tasks, we propose an intrinsic evaluation via clustering and evaluate user embeddings by an extrinsic task, text classification. The experiments on the three English-language social media datasets show that our proposed approach can generally outperform baselines via adapting the user factor.",
author = "Xiaolei Huang and Paul, {Michael J.} and Robin Burke and Franck Dernoncourt and Mark Dredze",
note = "Funding Information: The authors thank the anonymous reviews. This work was supported in part by the National Science Foundation under award number IIS-1657338. This work was also supported in part by a research gift from Adobe Research. The first author would thank the computational support from the JHU CLSP cluster. Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics; 2nd Workshop on Domain Adaptation for NLP, Adapt-NLP 2021 ; Conference date: 20-04-2021",
year = "2021",
language = "English (US)",
series = "Adapt-NLP 2021 - 2nd Workshop on Domain Adaptation for NLP, Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "172--182",
editor = "Eyal Ben-David and Shay Cohen and Ryan McDonald and Barbara Plank and Roi Reichart and Guy Rotman and Yftah Ziser",
booktitle = "Adapt-NLP 2021 - 2nd Workshop on Domain Adaptation for NLP, Proceedings",
address = "United States",
}