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.
Anthology ID:
2023.emnlp-main.1042
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
16765–16779
Language:
URL:
https://aclanthology.org/2023.emnlp-main.1042
DOI:
10.18653/v1/2023.emnlp-main.1042
Bibkey:
Cite (ACL):
Robert Ridley, Zhen Wu, Jianbing Zhang, Shujian Huang, and Xinyu Dai. 2023. Addressing Linguistic Bias through a Contrastive Analysis of Academic Writing in the NLP Domain. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 16765–16779, Singapore. Association for Computational Linguistics.
Cite (Informal):
Addressing Linguistic Bias through a Contrastive Analysis of Academic Writing in the NLP Domain (Ridley et al., EMNLP 2023)
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