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 sentencelevel tasks. Our code is available at https: //github.com/jabalazs/gating.
REF provide an empirical study showing the effects of different ways of combining character and word representations in word-level and sentence-level evaluation tasks.
Gating Mechanisms for Combining Character and Word-level Word Representations: An Empirical Study
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Computer Science Mathematics