论文标题
在多语言环境中讽刺检测
Irony Detection in a Multilingual Context
论文作者
论文摘要
本文提出了第一种多语言（法语，英语和阿拉伯语）和多元文化（印欧语语言与文化上较少的语言）的讽刺检测系统。我们使用单语词表示使用基于功能的模型和神经体系结构。我们将这些系统的性能与最先进的系统进行比较，以识别其功能。我们表明，这些单语模型使用多语言单词表示或基于文本的功能分别对不同语言进行了训练，可以打开讽刺性检测的大门，而语言缺乏带注释的数据的讽刺数据。
This paper proposes the first multilingual (French, English and Arabic) and multicultural (Indo-European languages vs. less culturally close languages) irony detection system. We employ both feature-based models and neural architectures using monolingual word representation. We compare the performance of these systems with state-of-the-art systems to identify their capabilities. We show that these monolingual models trained separately on different languages using multilingual word representation or text-based features can open the door to irony detection in languages that lack of annotated data for irony.
