Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers

Authors: Chia-Chien Hung, Anne Lauscher, Dirk Hovy, Simone Paolo Ponzetto, Goran Glavaš

EACL 2023. Findings: https://aclanthology.org/2023.findings-eacl.116/

Introduction

Demographic factors (e.g., gender or age) shape our language. Previous work showed that incorporating demographic factors can consistently improve performance for various NLP tasks with traditional NLP models. In this work, we investigate whether these previous findings still hold with state-of-the-art pretrained Transformer-based language models (PLMs). We use three common specialization methods proven effective for incorporating external knowledge into pretrained Transformers (e.g., domain-specific or geographic knowledge). We adapt the language representations for the demographic dimensions of gender and age, using continuous language modeling and dynamic multi-task learning for adaptation, where we couple language modeling objectives with the prediction of demographic classes. Our results, when employing a multilingual PLM, show substantial gains in task performance across four languages (English, German, French, and Danish), which is consistent with the results of previous work. However, controlling for confounding factors – primarily domain and language proficiency of Transformer-based PLMs – shows that downstream performance gains from our demographic adaptation do not actually stem from demographic knowledge. Our results indicate that demographic specialization of PLMs, while holding promise for positive societal impact, still represents an unsolved problem for (modern) NLP.

Citation

If you use any source codes, or datasets included in this repo in your work, please cite the following paper:

@inproceedings{hung-etal-2023-demographic,
    title = "Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers",
    author = "Hung, Chia-Chien  and
      Lauscher, Anne  and
      Hovy, Dirk  and
      Ponzetto, Simone Paolo  and
      Glava{\v{s}}, Goran",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-eacl.116",
    pages = "1565--1580",
}

Datasets

The datasets contain two main parts:

1. data used for intermediate training purpose, in order to encode knowledge via the sociodemographic-specific corpus. You can download the data from here.
2. data used for downstream tasks. You can download the data for gender and age.

Structure

This repository is currently under the following structure:

.
└── data
└── downstream
└── specialization
└── README.md