基于多粒度语义交互理解网络的幽默等级识别(A Multi-Granularity Semantic Interaction Understanding Network for Humor Level Recognition)

Jinhui Zhang (张瑾晖), Shaowu Zhang (张绍武), Xiaochao Fan (樊小超), Liang Yang (杨亮), Hongfei Lin (林鸿飞)


Abstract
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。
Anthology ID:
2020.ccl-1.60
Volume:
Proceedings of the 19th Chinese National Conference on Computational Linguistics
Month:
October
Year:
2020
Address:
Haikou, China
Venue:
CCL
SIG:
Publisher:
Chinese Information Processing Society of China
Note:
Pages:
645–655
Language:
Chinese
URL:
https://aclanthology.org/2020.ccl-1.60
DOI:
Bibkey:
Cite (ACL):
Jinhui Zhang, Shaowu Zhang, Xiaochao Fan, Liang Yang, and Hongfei Lin. 2020. 基于多粒度语义交互理解网络的幽默等级识别(A Multi-Granularity Semantic Interaction Understanding Network for Humor Level Recognition). In Proceedings of the 19th Chinese National Conference on Computational Linguistics, pages 645–655, Haikou, China. Chinese Information Processing Society of China.
Cite (Informal):
基于多粒度语义交互理解网络的幽默等级识别(A Multi-Granularity Semantic Interaction Understanding Network for Humor Level Recognition) (Zhang et al., CCL 2020)
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https://preview.aclanthology.org/ingestion-script-update/2020.ccl-1.60.pdf