论文标题
代码切换模式可能是提高下游NLP应用程序性能的有效途径：幽默，讽刺和仇恨言论检测的案例研究
Code-switching patterns can be an effective route to improve performance of downstream NLP applications: A case study of humour, sarcasm and hate speech detection
论文作者
论文摘要
在本文中，我们演示了如何利用代码切换模式来改善各种下游NLP应用程序。特别是，我们编码不同的切换功能，以改善幽默，讽刺和仇恨言语检测任务。我们认为，这种简单的语言观察也可能有助于改善其他类似的NLP应用程序。
In this paper we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications. In particular, we encode different switching features to improve humour, sarcasm and hate speech detection tasks. We believe that this simple linguistic observation can also be potentially helpful in improving other similar NLP applications.
