论文标题
上下文影响检测的深度神经框架
A Deep Neural Framework for Contextual Affect Detection
论文作者
论文摘要
简单而简单的文字携带不情感的文字在阅读及其上下文时可以代表一些强烈的情绪，即，相同的句子可以表达极端的愤怒，并取决于其上下文。在本文中，我们提出了一个上下文影响检测（CAD）框架，该框架学习了句子中单词的相互依存关系，同时句子与对话中句子的相互依存关系。我们提出的CAD框架是基于封闭式复发单元（GRU）的，该单元得到了上下文单词嵌入和其他多种手工制作的功能集的进一步协助。评估和分析表明，我们的模型在朋友和Emotionpush数据集上的表现分别优于最先进的方法和9.14％。
A short and simple text carrying no emotion can represent some strong emotions when reading along with its context, i.e., the same sentence can express extreme anger as well as happiness depending on its context. In this paper, we propose a Contextual Affect Detection (CAD) framework which learns the inter-dependence of words in a sentence, and at the same time the inter-dependence of sentences in a dialogue. Our proposed CAD framework is based on a Gated Recurrent Unit (GRU), which is further assisted by contextual word embeddings and other diverse hand-crafted feature sets. Evaluation and analysis suggest that our model outperforms the state-of-the-art methods by 5.49% and 9.14% on Friends and EmotionPush dataset, respectively.
