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Finding Concept-specific Biases in Form--Meaning Associations ...
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Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models ...
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Abstract:
While the prevalence of large pre-trained language models has led to significant improvements in the performance of NLP systems, recent research has demonstrated that these models inherit societal biases extant in natural language. In this paper, we explore a simple method to probe pre-trained language models for gender bias, which we use to effect a multi-lingual study of gender bias towards politicians. We construct a dataset of 250k politicians from most countries in the world and quantify adjective and verb usage around those politicians' names as a function of their gender. We conduct our study in 7 languages across 6 different language modeling architectures. Our results demonstrate that stance towards politicians in pre-trained language models is highly dependent on the language used. Finally, contrary to previous findings, our study suggests that larger language models do not tend to be significantly more gender-biased than smaller ones. ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning stat.ML
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URL: https://dx.doi.org/10.48550/arxiv.2104.07505
https://arxiv.org/abs/2104.07505
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A surprisal--duration trade-off across and within the world's languages ...
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What About the Precedent: An Information-Theoretic Analysis of Common Law ...
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| 15 |
Finding Concept-specific Biases in Form–Meaning Associations ...
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Disambiguatory Signals are Stronger in Word-initial Positions ...
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| 18 |
Modeling the Unigram Distribution
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In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
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What About the Precedent: An Information-Theoretic Analysis of Common Law
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In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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Finding Concept-specific Biases in Form–Meaning Associations
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In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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