Learning Noun Cases Using Sequential Neural Networks
This addresses data sparsity and flexibility issues in NLP for morphologically rich languages, but it is incremental as it builds on existing neural network methods.
The paper tackles the problem of morphological declension for nouns in morphologically rich languages, proposing to use Recurrent Neural Networks (RNNs) to learn noun cases and improve generalization in cross-lingual tasks.
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.