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。
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.
关键词
幽默等级识别 /
自然语言推理 /
多粒度 /
语义交互理解
{{custom_keyword}} /
Key words
humor level recognition /
natural language inference /
multi-granularity /
semantic interaction understanding
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Morse D R. Use of humor to reduce stress and pain and enhance healing in the dentalsetting.[J]. J N J Dent Assoc, 2007, 78(4):32-36.
[2] Mihalcea R. Making computers laugh: investigations in automatic humor recognition[C]//Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, 2005: 531-538.
[3] Zhang R, Liu N. Recognizing humor on twitter[C]//Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014: 889-898.
[4] Blinov V, Bolotova Baranova V, Braslavski P. Large dataset and language model fun-tuning for humor recognition[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 4027-4032.
[5] Weller O, SEPPI K. Humor detection: a transformer gets the last laugh[C]//Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 3612-3616.
[6] Hossain N, Krumm J, Gamon M. President vows to cut hair: dataset and analysis of creative text editing for humorous headlines[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019: 133-142.
[7] Paulos J A. Mathematics and humor[M]. Chicago, University of Chicago Press, 1980.
[8] Suls J M. A two-stage model for the appreciation of jokes and cartoons: an information-processing analysis[J]. The Psychology of Humor: Theoretical Perspectives and Empirical issues, 1972, 1: 81-100.
[9] RASKIN V. Semantic mechanisms of humor[C]//Proceedings of the Annual Meeting of the Berkeley Linguistics Society, 1979: 325-335.
[10] Yang D,Lavie A, Dyer C, et al. Humor recognition and humor anchor extraction[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2015: 2367-2376.
[11] Barbieri F,Saggion H. Automatic detection of irony and humour in twitter[J]. Process Biochemistry, 2014, 40(8):2637-2642.
[12] Liu L, Zhang. Modeling sentiment association in discourse for humor recognition[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 2018: 586-591.
[13] Liu L, Zhang. Exploiting syntactic structures for humor recognition[C]//Proceedings of the 27th International Conference on Computational Linguistics, 2018: 1875-1883.
[14] 杨勇,杨亮,邹艳波,等.基于音形义特征和层次注意力机制的幽默识别[J/OL].计算机工程: 2021: 1-12.https://doi.org/10.19678/j.issn.1000-3428.0057138.[2021-01-30].
[15] Bertero D, Fung P. A long short-term memory framework for predicting humor in dialogues[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016: 130-135.
[16] Bertero D, Fung P. Deep learning of audio and language features for humor prediction[C]//Proceedings of the Tenth International Conference on Language Resources and Evaluation, 2016: 496-501.
[17] Baziotis C, Pelekis N, Doulkeridis C. DataStories at SemEval-2017 Task 6: Siamese LSTM with attention for humorous text comparison[C]//Proceedings of the 11th International Workshop on Semantic Evaluation, 2017: 390-395.
[18] Zhao Z, Cattle A,Papalexakis E, et al. Embedding lexical features via tensor decomposition for small sample humor recognition[C]//Proceedings of the Conference on Empirical Methods in Natural Language, 2019: 6376-6381.
[19] Chiruzzo L, Castro S, Rosa A. HAHA 2019 Dataset: a corpus for humor analysis in Spanish[C]//Proceedings of the 12th Language Resources and Evaluation Conference, 2020: 5106-5112.
[20] Westbury C, Hollis G. Wriggly,squiffy, lummox, and boobs: What makes some words funny?[J]. Journal of Experimental Psychology: General, 2019, 148(1): 97.
[21] Cattle A, Ma X. Effects of semantic relatedness between setups and punchlines in twitter hashtag games[C]//Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, 2016: 70-79.
[22] Potash P, Romanov A,Rumshisky A. HashtagWars: learning a sense of humor[J]. arXiv: 1612.03216,2016.
[23] Xu H, Liu B, Shu L, et al. Double embeddings and CNN-based sequence labeling for aspect extraction[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018: 592-598.
[24] Pennington J,Socher R, Manning C. Glove: global vectors for word representation[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2014: 1532-1543.
[25] Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019: 4171-4186.
[26] Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780.
[27] Chen Q, Zhu X, Ling Z, et al. Enhanced LSTM for natural language inference[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2016: 1657-1668.
[28] Engelthaler T, Hills T T. Humor norms for 4,997 English words[J]. Behavior Research Methods, 2017, 50(1): 1-9.
[29] Ma D, Li S, Zhang X, et al. Interactive attention networks for aspect-level sentiment classification[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017: 4068-4074.
[30] Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiu: 1301.3781, 2013.
[31] Kingma D R, Ba J. Adam: a method for stochastic optimization[J]. arXiv preprint arXiv: 1412.6980, 2014.
[32] Yoon K. Convolutional neural networks for sentence classification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2014: 1746-1751.
[33] Vaswani A,Shazeer N, Parmar N, et al. Attention is all you need[C]//Proceedings of the Advances in Neural Information Processing Systems. 2017: 5998-6008.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
面向社交媒体的中文幽默计算研究(62076046);基于情感语义表示的隐式情感分析(61702080);基于媒体画像和防疫图谱的中国防疫形象评估和对策研究(21BXW047)
{{custom_fund}}
PDF(3647 KB)
