Learning Noun Cases Using Sequential Neural Networks

TL;DR

This paper investigates the effectiveness of Recurrent Neural Networks in learning noun declensions, focusing on morphological dependencies and generalization across languages in NLP tasks.

Contribution

It explores the application of RNNs to morphological declension, addressing data sparsity and sentence structure variability in morphologically rich languages.

Findings

RNNs can learn noun case inflections effectively.

Modeling morphological dependencies improves cross-lingual generalization.

Experiments reveal interpretable features aiding model performance.

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.