  Graph Convolutional Networks (GCN) have achieved state-of-art results on
single text classification tasks like sentiment analysis, emotion detection,
etc. However, the performance is achieved by testing and reporting on
resource-rich languages like English. Applying GCN for multi-task text
classification is an unexplored area. Moreover, training a GCN or adopting an
English GCN for Indian languages is often limited by data availability, rich
morphological variation, syntax, and semantic differences. In this paper, we
study the use of GCN for the Telugu language in single and multi-task settings
for four natural language processing (NLP) tasks, viz. sentiment analysis (SA),
emotion identification (EI), hate-speech (HS), and sarcasm detection (SAR). In
order to evaluate the performance of GCN with one of the Indian languages,
Telugu, we analyze the GCN based models with extensive experiments on four
downstream tasks. In addition, we created an annotated Telugu dataset, TEL-NLP,
for the four NLP tasks. Further, we propose a supervised graph reconstruction
method, Multi-Task Text GCN (MT-Text GCN) on the Telugu that leverages to
simultaneously (i) learn the low-dimensional word and sentence graph embeddings
from word-sentence graph reconstruction using graph autoencoder (GAE) and (ii)
perform multi-task text classification using these latent sentence graph
embeddings. We argue that our proposed MT-Text GCN achieves significant
improvements on TEL-NLP over existing Telugu pretrained word embeddings, and
multilingual pretrained Transformer models: mBERT, and XLM-R. On TEL-NLP, we
achieve a high F1-score for four NLP tasks: SA (0.84), EI (0.55), HS (0.83) and
SAR (0.66). Finally, we show our model's quantitative and qualitative analysis
on the four NLP tasks in Telugu.
