CAiRE_HKUST at SemEval-2019任务3：对话情绪分类的层次注意力
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification
摘要：
从对话中检测情感是一个尚未被广泛调查的挑战。人们可以认为每个对话的情绪是独立的，但在本文中，我们引入了一种分层的方法来分类情绪，假设当前的情绪状态取决于以前的潜在情绪。我们使用预先训练的单词和情感嵌入，最先进的端到端神经网络模型和自动超参数搜索的高斯过程对几个基于特征的分类器进行基准测试。在我们的实验中，分层架构始终提供显着的改进，我们最好的模型在测试集上获得了76.77%的F1分数。
英文摘要：
Detecting emotion from dialogue is a challenge that has not yet been extensively surveyed. One could consider the emotion of each dialogue turn to be independent, but in this paper, we introduce a hierarchical approach to classify emotion, hypothesizing that the current emotional state depends on previous latent emotions. We benchmark several feature-based classifiers using pre-trained word and emotion embeddings, state-of-the-art end-to-end neural network models, and Gaussian processes for automatic hyper-parameter search. In our experiments, hierarchical architectures consistently give significant improvements, and our best model achieves a 76.77% F1-score on the test set.
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。
