Sarcasm detection is an essential task that can help identify the actual sentiment in user-generated data, such as discussion forums or tweets. Sarcasm is a sophisticated form of linguistic expression because its surface meaning usually contradicts its inner, deeper meaning. Such incongruity is the essential component of sarcasm, however, it makes sarcasm detection quite a challenging task. In this paper, we propose a model, that incorporates different features to capture the incongruity intrinsic to sarcasm. We use a pre-trained transformer and CNN to capture context features, and we use transformers pre-trained on emotions detection and sentiment analysis tasks. Our approach outperformed previous state-of-the-art results on four datasets from social networking platforms and online media.
翻译:讽刺的探测是一项重要任务,可以帮助识别用户生成的数据中的实际情绪,如讨论论坛或推特。讽刺是一种复杂的语言表达形式,因为其表面含义通常与内心、更深层含义相矛盾。这种不协调性是讽刺的基本要素,但使讽刺性探测相当具有挑战性。在本文中,我们提出了一个模型,其中包含不同特征,以捕捉讽刺性所固有的不协调性。我们使用预先训练的变压器和CNN来捕捉背景特征,我们使用经过预先训练的变压器进行情感探测和情绪分析任务。我们的方法在社交网络平台和在线媒体的四套数据集上比以往的先进结果要好。