Towards an enhanced understanding of bias in pre-trained neural language models: a survey with special emphasis on affective bias

Anoop, K., P. Gangan, Manjary, Deepak, P. and Lajish, V.L. (2022) Towards an enhanced understanding of bias in pre-trained neural language models: a survey with special emphasis on affective bias. Mathew, J., Kumar, G.S., Deepak, P. and Jose, J.M. (eds.) In Responsible Data Science: Select Proceedings of ICDSE 2021. vol. 940, Springer Singapore. pp. 13-45 . (doi:10.1007/978-981-19-4453-6_2).

Abstract

The remarkable progress in Natural Language Processing (NLP) brought about by deep learning, particularly with the recent advent of large pre-trained neural language models, is brought into scrutiny 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 and analyze the stages at which they occur in these models, and various ways in which these biases could be quantified and mitigated. Considering wide applicability of textual affective computing-based downstream tasks in real-world systems such as business, health care, and education, 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 f

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Contributors

Author: K. Anoop

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