Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias
Anoop Kadan1, Manjary P. Gangan1, Deepak P.2 and Lajish V. L.1
1University of Calicut, Kerala, India
2Queen’s University Belfast, UK
📝 Paper : https://link.springer.com/chapter/10.1007/978-981-19-4453-6_2
📝 Pre-print: https://arxiv.org/abs/2204.10365
Abstract: Remarkable progress in Natural Language Processing (NLP) brought by deep learning, particularly with the recent advent of large pre-trained neural language models, is injured as several studies began to discuss and report potential biases in NLP applications. Bias in NLP is found to originate from latent historical biases encoded by humans into textual data which gets perpetuated or even amplified by NLP algorithm. We present a survey to comprehend bias in large pre-trained language models, the stages at which they occur in these models, and various ways in which these biases are quantified and mitigated. Considering wide applicability of textual affective computing based downstream tasks in real-world systems such as business, healthcare, education, etc., we give a special emphasis on investigating bias in the context of affect (emotion) i.e., Affective Bias, in large pre-trained language models. We present a summary of various bias evaluation corpora that help to aid future research and discuss challenges in the research on bias in pretrained language models. We believe that our attempt to draw a comprehensive view of bias in pre-trained language models, and especially the exploration of affective bias will be highly beneficial to acquire deep knowledge on recent paradigms in this area of research.
For inquiries, please contact:
Anoop Kadan, University of Southampton, UK. 📧 [email protected] 🌏 website
Manjary P. Gangan, University of Calicut, Kerala, India. 📧 [email protected] 🌏 website
@InProceedings{anoop2022towards,
author={Kadan, Anoop and P. Gangan, Manjary and P., Deepak and V. L., Lajish},
title="{Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias},
booktitle={Responsible Data Science},
year={2022},
publisher={Springer Nature Singapore},
address={Singapore},
pages={13--45},
isbn={978-981-19-4453-6},
doi={https://doi.org/10.1007/978-981-19-4453-6_2}
}