Multi-task Representation Learning for Enhanced Emotion Categorization in Short Text

Embedding based dense contextual representations of data have proven to be efficient in various NLP tasks as they alleviate the burden of heavy feature engineering. However, generalized representation learning approaches do not capture the task specific subtleties. In addition, often the computational model for each task is developed in isolation, overlooking the interrelation among certain NLP tasks. Given that representation learning typically requires a good amount of labeled annotated data which is scarce, it is essential to explore learning embedding under supervision of multiple related tasks jointly and at the same time, incorporating the task specific attributes too. Inspired by the basic premise of multi-task learning, which supposes that correlation between related tasks can be used to improve classification, we propose a novel technique for building jointly learnt task specific embeddings for emotion and sentiment prediction tasks. Here, a sentiment prediction task acts as an auxiliary input to enhance the primary emotion prediction task. Our experimental results demonstrate that embeddings learnt under supervised signals of two related tasks, outperform embeddings learnt in a uni-tasked setup for the downstream task of emotion prediction.