论文标题
针对目标情感分类的否定和投机的多任务学习
Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification
论文作者
论文摘要
有针对性的情感分析中的大多数工作集中在寻找更好的方法来改善总体结果。在本文中，我们表明，这些模型对语言现象不强大，特别是否定和猜测。在本文中，我们提出了一种多任务学习方法，以合并来自句法和语义辅助任务的信息，包括否定和投机范围检测，以创建英语模型，这些模型对这些现象更强大。此外，我们创建了两个挑战数据集，以评估否定和投机样本上的模型性能。我们发现，多任务模型和通过语言建模的转移学习可以提高这些挑战数据集的性能，但总体表现表明，仍然有很大的改进空间。我们在https://github.com/jerbarnes/multitask_negation_for_for_targeted_sentiment上同时发布数据集和源代码。
The majority of work in targeted sentiment analysis has concentrated on finding better methods to improve the overall results. Within this paper we show that these models are not robust to linguistic phenomena, specifically negation and speculation. In this paper, we propose a multi-task learning method to incorporate information from syntactic and semantic auxiliary tasks, including negation and speculation scope detection, to create English-language models that are more robust to these phenomena. Further we create two challenge datasets to evaluate model performance on negated and speculative samples. We find that multi-task models and transfer learning via language modelling can improve performance on these challenge datasets, but the overall performances indicate that there is still much room for improvement. We release both the datasets and the source code at https://github.com/jerbarnes/multitask_negation_for_targeted_sentiment.
