Sentiment and Sarcasm Classification with Multitask Learning
This addresses the problem of separate modeling for sentiment and sarcasm in NLP, offering a combined approach that is incremental but provides measurable gains.
The paper tackles sentiment classification and sarcasm detection as correlated NLP tasks, proposing a multi-task learning framework that improves performance on both tasks by 3-4% over state-of-the-art methods on a benchmark dataset.
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.