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• W2963280802 abstract "In this paper we study how different ways of combining character and word-level representations affect the quality of both final word and sentence representations. We provide strong empirical evidence that modeling characters improves the learned representations at the word and sentence levels, and that doing so is particularly useful when representing less frequent words. We further show that a feature-wise sigmoid gating mechanism is a robust method for creating representations that encode semantic similarity, as it performed reasonably well in several word similarity datasets. Finally, our findings suggest that properly capturing semantic similarity at the word level does not consistently yield improved performance in downstream sentence-level tasks." @default.
• W2963280802 created "2019-07-30" @default.
• W2963280802 creator A5001821439 @default.
• W2963280802 creator A5074059447 @default.
• W2963280802 date "2019-01-01" @default.
• W2963280802 modified "2023-09-30" @default.
• W2963280802 title "Gating Mechanisms for Combining Character and Word-level Word Representations: an Empirical Study" @default.
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• W2963280802 doi "https://doi.org/10.18653/v1/n19-3016" @default.
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