【摘要】 幽默在人们日常交流中发挥着重要作用。随着人工智能的快速发展，幽默等级识别成为自然语言处理领域的热点研究问题之一。已有的幽默等级识别研究往往将幽默文本看作一个整体，忽视了幽默文本内部的语义关系。该文将幽默等级识别视为自然语言推理任务，将幽默文本划分为“铺垫”和“笑点”两个部分，分别对其语义和语义关系进行建模，提出了一种多粒度语义交互理解网络，从单词和子句两个粒度捕获幽默文本中语义的关联和交互。在Reddit公开幽默数据集上进行了实验，相比之前最优结果，模型在语料上的准确率提升了1.3%。实验表明，引入幽默文本内部的语义关系信息可以提高模型的幽默识别性能，而该文提出的模型也可以很好地建模这种语义关系。更多还原

【Abstract】 Humor plays an important role in daily communication. Existing works of humor level recognition tend to treat humor text as a whole, ignoring the inner semantic relations of it. Treating humor level recognition as a kind of natural language inference task, this paper divides humor text into two parts: "setup" and "punchline", and captures them with their mutual relations. A multi-granularity semantic interaction understanding network is proposes to capture semantic association and interaction in humor text from both word and clause granularity. We conduct experiments on public humor data set Reddit, and the accuracy of the model on this corpus is improved by 1.3% compared with the previous optimal results.更多还原