LTG-Oslo分层多任务网络：否定对西班牙语文档级情感的重要性
LTG-Oslo Hierarchical Multi-task Network: The importance of negation for document-level sentiment in Spanish
摘要：
本文详细介绍了LTG-Oslo团队参与NEGES 2019评估活动的情绪跟踪。我们使用分层多任务网络参与任务，该网络使用深度BiLSTM中的共享较低层来预测否定，而较高层则致力于预测文档级情感。多任务组件显示出将否定信息纳入深度神经情感分类器的希望，尽管测试集上的绝对结果对于二进制分类任务来说相对较低。
英文摘要：
This paper details LTG-Oslo team's participation in the sentiment track of the NEGES 2019 evaluation campaign. We participated in the task with a hierarchical multi-task network, which used shared lower-layers in a deep BiLSTM to predict negation, while the higher layers were dedicated to predicting document-level sentiment. The multi-task component shows promise as a way to incorporate information on negation into deep neural sentiment classifiers, despite the fact that the absolute results on the test set were relatively low for a binary classification task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
