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Title: Neural User Factor Adaptation for Text Classification: Learning to Generalize Across Author Demographics
Language use varies across different demographic factors, such as gender, age, and geographic location. However, most existing document classification methods ignore demographic variability. In this study, we examine empirically how text data can vary across four demographic factors: gender, age, country, and region. We propose a multitask neural model to account for demographic variations via adversarial training. In experiments on four English-language social media datasets, we find that classification performance improves when adapting for user factors.
Authors:
;
Award ID(s):
1657338
Publication Date:
NSF-PAR ID:
10112015
Journal Name:
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
Page Range or eLocation-ID:
136 to 146
Sponsoring Org:
National Science Foundation
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