Natural Language Processing (NLP) and especially natural language text
analysis have seen great advances in recent times. Usage of deep learning in
text processing has revolutionized the techniques for text processing and
achieved remarkable results. Different deep learning architectures like CNN,
LSTM, and very recent Transformer have been used to achieve state of the art
results variety on NLP tasks. In this work, we survey a host of deep learning
architectures for text classification tasks. The work is specifically concerned
with the classification of Hindi text. The research in the classification of
morphologically rich and low resource Hindi language written in Devanagari
script has been limited due to the absence of large labeled corpus. In this
work, we used translated versions of English data-sets to evaluate models based
on CNN, LSTM and Attention. Multilingual pre-trained sentence embeddings based
on BERT and LASER are also compared to evaluate their effectiveness for the
Hindi language. The paper also serves as a tutorial for popular text
classification techniques.

本研究旨在调查各种深度学习架构在印地语文本分类任务中的应用。本文使用了卷积神经网络（CNN）、长短时记忆网络（LSTM）、注意力机制等模型，比较了多语言预训练句子嵌入（BERT 和 LASER）和传统翻译方法的优劣，也为流行的文本分类技术提供了教程。