User factor adaptation for user embedding via multitask learning

Xiaolei Huang, Michael J. Paul, Robin Burke, Franck Dernoncourt, Mark Dredze

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationAdapt-NLP 2021 - 2nd Workshop on Domain Adaptation for NLP, Proceedings
EditorsEyal Ben-David, Shay Cohen, Ryan McDonald, Barbara Plank, Roi Reichart, Guy Rotman, Yftah Ziser
PublisherAssociation for Computational Linguistics (ACL)
Pages172-182
Number of pages11
ISBN (Electronic)9781954085084
StatePublished - 2021
Event2nd Workshop on Domain Adaptation for NLP, Adapt-NLP 2021 - Kyiv, Ukraine
Duration: Apr 20 2021 → …

Publication series

NameAdapt-NLP 2021 - 2nd Workshop on Domain Adaptation for NLP, Proceedings

Conference

Conference2nd Workshop on Domain Adaptation for NLP, Adapt-NLP 2021
Country/TerritoryUkraine
CityKyiv
Period4/20/21 → …

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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