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• W3113754522 abstract "While pre-trained word embeddings have been shown to improve the performance of downstream tasks, many questions remain regarding their reliability: Do the same pre-trained word embeddings result in the best performance with slight changes to the training data? Do the same pre-trained embeddings perform well with multiple neural network architectures? Do imputation strategies for unknown words impact reliability? In this paper, we introduce two new metrics to understand the downstream reliability of word embeddings. We find that downstream reliability of word embeddings depends on multiple factors, including, the evaluation metric, the handling of out-of-vocabulary words, and whether the embeddings are fine-tuned." @default.
• W3113754522 created "2021-01-05" @default.
• W3113754522 creator A5018773113 @default.
• W3113754522 creator A5052954988 @default.
• W3113754522 date "2020-01-01" @default.
• W3113754522 modified "2023-10-18" @default.
• W3113754522 title "An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings" @default.
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• W3113754522 doi "https://doi.org/10.18653/v1/2020.coling-main.299" @default.
• W3113754522 hasPublicationYear "2020" @default.
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