@inproceedings{huang-paul-2019-neural,
title = "Neural User Factor Adaptation for Text Classification: Learning to Generalize Across Author Demographics",
author = "Huang, Xiaolei and
Paul, Michael J.",
editor = "Mihalcea, Rada and
Shutova, Ekaterina and
Ku, Lun-Wei and
Evang, Kilian and
Poria, Soujanya",
booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-1015",
doi = "10.18653/v1/S19-1015",
pages = "136--146",
abstract = "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.",
}
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%0 Conference Proceedings
%T Neural User Factor Adaptation for Text Classification: Learning to Generalize Across Author Demographics
%A Huang, Xiaolei
%A Paul, Michael J.
%Y Mihalcea, Rada
%Y Shutova, Ekaterina
%Y Ku, Lun-Wei
%Y Evang, Kilian
%Y Poria, Soujanya
%S Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F huang-paul-2019-neural
%X 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.
%R 10.18653/v1/S19-1015
%U https://aclanthology.org/S19-1015
%U https://doi.org/10.18653/v1/S19-1015
%P 136-146
Markdown (Informal)
[Neural User Factor Adaptation for Text Classification: Learning to Generalize Across Author Demographics](https://aclanthology.org/S19-1015) (Huang & Paul, *SEM 2019)
ACL