Sarcasm Detection with External Entity Information

Xu Xufei, Shimada Kazutaka
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引用次数: 0

Abstract

Sarcasm is generally characterized as ironic or satirical that is intended to blame, mock, or amuse in an implied way. Recently, pre-trained language models, such as BERT, have achieved remarkable success in sarcasm detection. However, there are many problems that cannot be solved by using such state-of-the-art models. One problem is attribute infor- mation of entities in sentences. This work investigates the potential of external knowledge about entities in knowledge bases to improve BERT for sarcasm detection. We apply em- bedded knowledge graph from Wikipedia to the task. We generate vector representations from entities of knowledge graph. Then we incorporate them with BERT by a mechanism based on self-attention. Experimental results indicate that our approach improves the accuracy as compared with the BERT model without external knowledge.
基于外部实体信息的讽刺检测
Sarcasm通常被描述为讽刺或讽刺,意在以一种隐含的方式指责、嘲笑或逗乐。近年来,预训练语言模型,如BERT,在讽刺检测方面取得了显著的成功。然而,有许多问题无法用最先进的模型来解决。一个问题是句子中实体的属性信息。这项工作研究了关于知识库中实体的外部知识的潜力,以改进BERT的讽刺检测。我们将维基百科的嵌入式知识图谱应用到任务中。我们从知识图谱的实体中生成向量表示。然后我们通过一种基于自我关注的机制将它们与BERT结合起来。实验结果表明,与没有外部知识的BERT模型相比,我们的方法提高了准确率。
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CiteScore
1.60
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