Abstract
In this paper, we explore various approaches for learning two types of appraisal components from happy language. We focus on 'agency' of the author and the 'sociality' involved in happy moments based on the HappyDB dataset. We develop models based on deep neural networks for the task, including uni- and bi-directional long short-term memory networks, with and without attention. We also experiment with a number of novel embedding methods, such as embedding from neural machine translation (as in CoVe) and embedding from language models (as in ELMo). We compare our results to those acquired by several traditional machine learning methods. Our best models achieve 87.97% accuracy on agency and 93.13% accuracy on sociality, both of which are significantly higher than our baselines.
Abstract (translated)
本文探讨了从快乐语言中学习两种评价成分的方法。我们关注的是作者的“代理”和基于happydb数据集的快乐时刻所涉及的“社会性”。我们开发了基于深度神经网络的任务模型，包括单向和双向的长期短期记忆网络，无论有无关注。我们还实验了一些新的嵌入方法，例如从神经机器翻译（如CoVe）中嵌入和从语言模型中嵌入（如在ELMo）。我们将我们的结果与几种传统机器学习方法得到的结果进行了比较。我们的最佳模型在代理方面的准确率达到87.97%，在社交方面的准确率达到93.13%，这两个方面都显著高于我们的基准。
URL
https://arxiv.org/abs/1906.03677
