Learning Noun Cases Using Sequential Neural Networks

Sina Ahmadi

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arXiv · 1810.03996

Learning Noun Cases Using Sequential Neural Networks

Sina Ahmadi

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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.

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BibTeX RIS

Sina Ahmadi. 2018-10-09. Learning Noun Cases Using Sequential Neural Networks. https://arxiv.org/abs/1810.03996

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