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- W2998368506 abstract "Word embeddings, trained through models like skip-gram, have shown to be prone to capturing the biases from the training corpus, e.g. gender bias. Such biases are unwanted as they spill in downstream tasks, thus, leading to discriminatory behavior." @default.
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- W2998368506 date "2020-01-20" @default.
- W2998368506 modified "2023-10-14" @default.
- W2998368506 title "Debiasing Word Embeddings from Sentiment Associations in Names" @default.
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- W2998368506 doi "https://doi.org/10.1145/3336191.3371779" @default.
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