Addressing Linguistic Bias through a Contrastive Analysis of Academic Writing in the NLP Domain
Robert Ridley, Zhen Wu, Jianbing Zhang, Shujian Huang, Xinyu Dai
Abstract
It has been well documented that a reviewer’s opinion of the nativeness of expression in an academic paper affects the likelihood of it being accepted for publication. Previous works have also shone a light on the stress and anxiety authors who are non-native English speakers experience when attempting to publish in international venues. We explore how this might be a concern in the field of Natural Language Processing (NLP) through conducting a comprehensive statistical analysis of NLP paper abstracts, identifying how authors of different linguistic backgrounds differ in the lexical, morphological, syntactic and cohesive aspects of their writing. Through our analysis, we identify that there are a number of characteristics that are highly variable across the different corpora examined in this paper. This indicates potential for the presence of linguistic bias. Therefore, we outline a set of recommendations to publishers of academic journals and conferences regarding their guidelines and resources for prospective authors in order to help enhance inclusivity and fairness.
BibTeX
@inproceedings{
ridley2023addressing,
title={Addressing Linguistic Bias through a Contrastive Analysis of Academic Writing in the {NLP} Domain},
author={Robert Ridley and Zhen Wu and Jianbing Zhang and Shujian Huang and Xinyu Dai},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=VJenYElbmY}
}