BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation

Tianxiang Sun, Junliang He, Xipeng Qiu, Xuanjing Huang


Abstract
Automatic evaluation metrics are crucial to the development of generative systems. In recent years, pre-trained language model (PLM) based metrics, such as BERTScore, have been commonly adopted in various generation tasks. However, it has been demonstrated that PLMs encode a range of stereotypical societal biases, leading to a concern about the fairness of PLMs as metrics. To that end, this work presents the first systematic study on the social bias in PLM-based metrics. We demonstrate that popular PLM-based metrics exhibit significantly higher social bias than traditional metrics on 6 sensitive attributes, namely race, gender, religion, physical appearance, age, and socioeconomic status. In-depth analysis suggests that choosing paradigms (matching, regression, or generation) of the metric has a greater impact on fairness than choosing PLMs. In addition, we develop debiasing adapters that are injected into PLM layers, mitigating bias in PLM-based metrics while retaining high performance for evaluating text generation.
Anthology ID:
2022.emnlp-main.245
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3726–3739
Language:
URL:
https://aclanthology.org/2022.emnlp-main.245
DOI:
Bibkey:
Cite (ACL):
Tianxiang Sun, Junliang He, Xipeng Qiu, and Xuanjing Huang. 2022. BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3726–3739, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation (Sun et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.245.pdf