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• W2606657156 abstract "Labeled sequence transduction is a task of transforming one sequence into another sequence that satisfies desiderata specified by a set of labels. In this paper we propose multi-space variational encoder-decoders, a new model for labeled sequence transduction with semi-supervised learning. The generative model can use neural networks to handle both discrete and continuous latent variables to exploit various features of data. Experiments show that our model provides not only a powerful supervised framework but also can effectively take advantage of the unlabeled data. On the SIGMORPHON morphological inflection benchmark, our model outperforms single-model state-of-art results by a large margin for the majority of languages." @default.
• W2606657156 created "2017-04-28" @default.
• W2606657156 creator A5052828977 @default.
• W2606657156 creator A5068811427 @default.
• W2606657156 date "2017-04-05" @default.
• W2606657156 modified "2023-09-25" @default.
• W2606657156 title "Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction" @default.
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• W2606657156 doi "https://doi.org/10.48550/arxiv.1704.01691" @default.
• W2606657156 hasPublicationYear "2017" @default.
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