Multi-Task Learning for Sentiment Analysis with Hard-Sharing and Task Recognition Mechanisms
Jian Zhang, Ke Yan, Yuchang Mo
Abstract
In the era of big data, multi-task learning has become one of the crucial technologies for sentiment analysis and classification. Most of the existing multi-task learning models for sentiment analysis are developed based on the soft-sharing mechanism that has less interference between different tasks than the hard-sharing mechanism. However, there are also fewer essential features that the model can extract with the soft-sharing method, resulting in unsatisfactory classification performance. In this paper, we propose a multi-task learning framework based on a hard-sharing mechanism for sentiment analysis in various fields. The hard-sharing mechanism is achieved by a shared layer to build the interrelationship among multiple tasks. Then, we design a task recognition mechanism to reduce the interference of the hard-shared feature space and also to enhance the correlation between multiple tasks. Experiments on two real-world sentiment classification datasets show that our approach achieves the best results and improves the classification accuracy over the existing methods significantly. The task recognition training process enables a unique representation of the features of different tasks in the shared feature space, providing a new solution reducing interference in the shared feature space for sentiment analysis.