Sentiment analysis is directly affected by compositional phenomena in language that act on the prior polarity of the words and phrases found in the text. Negation is the most prevalent of these phenomena, and in order to correctly predict sentiment, a classifier must be able to identify negation and disentangle the effect that its scope has on the final polarity of a text. This paper proposes a multi-task approach to explicitly incorporate information about negation in sentiment analysis, which we show outperforms learning negation implicitly in an end-to-end manner. We describe our approach, a cascading and hierarchical neural architecture with selective sharing of Long Short-term Memory layers, and show that explicitly training the model with negation as an auxiliary task helps improve the main task of sentiment analysis. The effect is demonstrated across several different standard English-language data sets for both tasks, and we analyze several aspects of our system related to its performance, varying types and amounts of input data and different multi-task setups.

中文翻译：

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通过否定的多任务学习改进情感分析

情感分析直接受到语言中的成分现象的影响，这些现象作用于文本中单词和短语的先前极性。否定是这些现象中最普遍的一种，为了正确预测情绪，分类器必须能够识别否定并解开其范围对文本最终极性的影响。本文提出了一种多任务方法，可以将有关否定的信息明确地纳入情感分析中，我们展示了这种方法以端到端的方式隐式地优于学习否定。我们描述了我们的方法，一种具有选择性共享长短期记忆层的级联和分层神经架构，并表明使用否定作为辅助任务显式训练模型有助于改进情感分析的主要任务。在这两个任务的几个不同的标准英语数据集上展示了这种效果，我们分析了我们系统的几个与其性能相关的方面，