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Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation ...
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Abstract:
Is bias amplified when neural machine translation (NMT) models are optimized for speed and evaluated on generic test sets using BLEU? We investigate architectures and techniques commonly used to speed up decoding in Transformer-based models, such as greedy search, quantization, average attention networks (AANs) and shallow decoder models and show their effect on gendered noun translation. We construct a new gender bias test set, SimpleGEN, based on gendered noun phrases in which there is a single, unambiguous, correct answer. While we find minimal overall BLEU degradation as we apply speed optimizations, we observe that gendered noun translation performance degrades at a much faster rate. ... : Accepted at ACL 2021 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2106.00169 https://dx.doi.org/10.48550/arxiv.2106.00169
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Investigating Failures of Automatic Translation in the Case of Unambiguous Gender ...
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Gender bias amplification during Speed-Quality optimization in Neural Machine Translation ...
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XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment ...
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Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data ...
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An exploratory study on multilingual quality estimation
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In: 366 ; 377 (2020)
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