论文标题
对悲伤和抑郁的细粒度分类的解释性
Interpretability of Fine-grained Classification of Sadness and Depression
论文作者
论文摘要
尽管悲伤是人们一生中某些时候经历的人类情感，使他们感到情感上的失望和痛苦，但抑郁症是一种长期的精神疾病，会损害社交，职业和其他重要的功能，使其成为一个更加严重的问题，并且需要最早迎合最严重的问题。 NLP技术可用于检测和随后诊断这些情绪。在网络交易中，大多数以悲伤作为抑郁症的一部分的开源数据，即使两者的严重程度的差异都是巨大的。因此，我们创建了自己的小说数据集，说明了两者之间的差异。在本文中，我们旨在强调两者之间的区别，并突出我们的模型对明显标记悲伤和沮丧的解释。由于此类信息的敏感性，需要采取隐私措施来处理和培训此类数据。因此，我们还探讨了联合学习（FL）对上下文化语言模型的影响。
While sadness is a human emotion that people experience at certain times throughout their lives, inflicting them with emotional disappointment and pain, depression is a longer term mental illness which impairs social, occupational, and other vital regions of functioning making it a much more serious issue and needs to be catered to at the earliest. NLP techniques can be utilized for the detection and subsequent diagnosis of these emotions. Most of the open sourced data on the web deal with sadness as a part of depression, as an emotion even though the difference in severity of both is huge. Thus, we create our own novel dataset illustrating the difference between the two. In this paper, we aim to highlight the difference between the two and highlight how interpretable our models are to distinctly label sadness and depression. Due to the sensitive nature of such information, privacy measures need to be taken for handling and training of such data. Hence, we also explore the effect of Federated Learning (FL) on contextualised language models.
