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.

本文提出了一种基于深度多任务学习的架构，通过使用预训练的 BERT 和 Glove 词向量嵌入，采用基于注意力机制的多任务模型，实现了情感极性和主观性检测，并且与先前的单任务和多任务模型进行了比较，提高了基准性能。