论文标题
多任务学习和BERT嵌入的极性和主观性检测
Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding
论文作者
论文摘要
多任务学习通常有助于提高相关任务的性能，因为这些任务通常相互依赖，并且在联合框架中解决时表现更好。在本文中，我们提出了一个深层的多任务学习框架，该框架共同执行极性和主观检测。我们提出了一个基于注意力的多任务模型，以预测极性和主观性。使用预训练的BERT和手套嵌入将输入句子转换为向量，结果描述了基于Bert嵌入的模型比基于手套的模型更好。我们将我们的方法与主观和极性分类单任务和多任务框架中的最先进模型进行了比较。该方法报告了极性检测和主观性检测的基线表现。
Multitask learning often helps improve the performance of related tasks as these often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multitask learning framework that jointly performs polarity and subjective detection. We propose an attention-based multitask model for predicting polarity and subjectivity. The input sentences are transformed into vectors using pre-trained BERT and Glove embeddings, and the results depict that BERT embedding based model works better than the Glove based model. We compare our approach with state-of-the-art models in both subjective and polarity classification single-task and multitask frameworks. The proposed approach reports baseline performances for both polarity detection and subjectivity detection.
