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。本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。本文在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,引入幽默内部的语义关系信息可以提高模型幽默识别的性能,而本文提出的模型也可以很好地建模这种语义关系。

In our daily life, metaphor is a common way of expression. To understand the meaning of a metaphor, we should recognize the metaphor words which play important roles. In the metaphor detection task, we design a sequence labeling model based on ALBERT-LSTM-softmax. By applying this model, we carry out a lot of experiments and compare the experimental results with different processing methods, such as with different input sentences and tokens, or the methods with CRF and softmax. Then, some tricks are adopted to improve the experimental results. Finally, our model achieves a 0.707 F1-score for the all POS subtask and a 0.728 F1-score for the verb subtask on the TOEFL dataset.