论文标题
相关任务可以共享！情感语言的多任务框架
Related Tasks can Share! A Multi-task Framework for Affective language
论文作者
论文摘要
与强度/极性相比，表达情感的极性为“正”和“负”通常具有有限的范围。这两个任务（即情感分类和情感强度预测）密切相关，并且可能在学习过程中为彼此提供帮助。在本文中，我们建议在多任务学习框架中利用多个任务的相关性。我们的多任务模型基于卷积门控复发单元（GRU）框架，该框架由多样化的手工制作的功能集进一步协助。评估和分析表明，多任务框架中相关任务的联合学习可以胜过单任务框架中的每个单个任务。
Expressing the polarity of sentiment as 'positive' and 'negative' usually have limited scope compared with the intensity/degree of polarity. These two tasks (i.e. sentiment classification and sentiment intensity prediction) are closely related and may offer assistance to each other during the learning process. In this paper, we propose to leverage the relatedness of multiple tasks in a multi-task learning framework. Our multi-task model is based on convolutional-Gated Recurrent Unit (GRU) framework, which is further assisted by a diverse hand-crafted feature set. Evaluation and analysis suggest that joint-learning of the related tasks in a multi-task framework can outperform each of the individual tasks in the single-task frameworks.
