  Sentiment classification and sarcasm detection are both important natural
language processing (NLP) tasks. Sentiment is always coupled with sarcasm where
intensive emotion is expressed. Nevertheless, most literature considers them as
two separate tasks. We argue that knowledge in sarcasm detection can also be
beneficial to sentiment classification and vice versa. We show that these two
tasks are correlated, and present a multi-task learning-based framework using a
deep neural network that models this correlation to improve the performance of
both tasks in a multi-task learning setting. Our method outperforms the state
of the art by 3-4% in the benchmark dataset.
