Bar Iluz
2024
Applying Intrinsic Debiasing on Downstream Tasks: Challenges and Considerations for Machine Translation
Bar Iluz
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Yanai Elazar
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Asaf Yehudai
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Gabriel Stanovsky
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Most works on gender bias focus on intrinsic bias — removing traces of information about a protected group from the model’s internal representation. However, these works are often disconnected from the impact of such debiasing on downstream applications, which is the main motivation for debiasing in the first place. In this work, we systematically test how methods for intrinsic debiasing affect neural machine translation models, by measuring the extrinsic bias of such systems under different design choices. We highlight three challenges and mismatches between the debiasing techniques and their end-goal usage, including the choice of embeddings to debias, the mismatch between words and sub-word tokens debiasing, and the effect on different target languages. We find that these considerations have a significant impact on downstream performance and the success of debiasing.
2023
Exploring the Impact of Training Data Distribution and Subword Tokenization on Gender Bias in Machine Translation
Bar Iluz
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Tomasz Limisiewicz
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Gabriel Stanovsky
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David Mareček
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
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