  Neural network models have shown promising results for text classification.
However, these solutions are limited by their dependence on the availability of
annotated data.
  The prospect of leveraging resource-rich languages to enhance the text
classification of resource-poor languages is fascinating. The performance on
resource-poor languages can significantly improve if the resource availability
constraints can be offset. To this end, we present a twin Bidirectional Long
Short Term Memory (Bi-LSTM) network with shared parameters consolidated by a
contrastive loss function (based on a similarity metric). The model learns the
representation of resource-poor and resource-rich sentences in a common space
by using the similarity between their assigned annotation tags. Hence, the
model projects sentences with similar tags closer and those with different tags
farther from each other. We evaluated our model on the classification tasks of
sentiment analysis and emoji prediction for resource-poor languages - Hindi and
Telugu and resource-rich languages - English and Spanish. Our model
significantly outperforms the state-of-the-art approaches in both the tasks
across all metrics.
