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- W2897544738 abstract "Morphological declension, which aims to inflect nouns to indicate number, case and gender, is an important task in natural language processing (NLP). This research proposal seeks to address the degree to which Recurrent Neural Networks (RNNs) are efficient in learning to decline noun cases. Given the challenge of data sparsity in processing morphologically rich languages and also, the flexibility of sentence structures in such languages, we believe that modeling morphological dependencies can improve the performance of neural network models. It is suggested to carry out various experiments to understand the interpretable features that may lead to a better generalization of the learned models on cross-lingual tasks." @default.
- W2897544738 created "2018-10-26" @default.
- W2897544738 creator A5064336794 @default.
- W2897544738 date "2018-10-09" @default.
- W2897544738 modified "2023-09-27" @default.
- W2897544738 title "Learning Noun Cases Using Sequential Neural Networks" @default.
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- W2897544738 hasPublicationYear "2018" @default.
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