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研究生:陳鵬宇
研究生(外文):Chen, Peng-Yu
論文名稱:利用深度學習之笑話辨識與生成
論文名稱(外文):Humor Recognition and Generation Using Deep Learning
指導教授:蘇豐文蘇豐文引用關係
指導教授(外文):Soo, Von-Wun
口試委員:陳煥宗陳宜欣
口試委員(外文):Chen, Hwann-TzongChen, Yi-Shin
口試日期:2018-07-23
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:36
中文關鍵詞:幽默辨識幽默生成深度學習自然語言處理
外文關鍵詞:Humor RecongnitionHumor GenerationDeep LearningNatural Language Processing
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幽默作為一個特殊的語意表達方式,是生活中活躍氣氛、化解尷尬的重要元 素。近年來隨著人工智慧的快速發展,深度學習在自然語言處理的許多任務 中,取得了不錯的成果,如何利用電腦技術識別和生成幽默,也成為自然語 言處理領域熱門的研究內容之一。在本論文中,我們構建並收集了四個具 有不同笑話類型的中英文語料庫,並進行了幽默識別及幽默生成的研究。 我們在本論文中提出了一個基於深度學習裡的卷積神經網路 (Convolutional Neural Network)的模型,並結合Highway Networks的技術訓練深層的網路來進 行幽默辨識的研究,實驗結果顯示,我們的深度學習模型在識別不同類型及 語言的幽默方面,表現皆優於以前的基準,並達到了大約九成的準確率。除 了幽默辨識之外,我們也進行了幽默生成相關研究,利用生成式對抗網路 (Generative Adversarial Networks)與強化學習相結合 (Reinforcement Learning)的模 型,來產生蘊含幽默語意的文章,我們改進並提出了一個包含了兩個鑑別器 (Discriminator)的生成式對抗網路架構,來使模型更好地生成幽默文章,除此之 外,並進行了嚴謹的比較評估,來探討如何使用深度學習進行幽默生成。
Computational humor has been a fascinating topic that poses great challenge to artificial intelligence. For computers to understand and tell jokes does not seem to be an trivial task that remains to be a mystery. There have been very few attempts in literature that discuss how to build computational models in either discovering the structures of hu- mor, recognizing humor or even generating humor. In this thesis, I construct and collect four datasets with distinct joke types in both English and Chinese and conduct learn- ing experiments on humor recognition. I implement a Convolutional Neural Network (CNN) with extensive filter size, number and Highway Networks to increase the depth of networks. Results show that our model outperforms in recognition of different types of humor with benchmarks collected in both English and Chinese languages on accu- racy, precision, and recall in comparison to previous works. In addition to recognition, we also conduct research on humor generation that utilize adversarial networks combine with reinforcement learning (policy gradient) to generate humorous text. We purpose a two discriminators architecture that indicate more precisely rewards for generator to improve learning and produce quality jokes.
Contents
1 Introduction ......................................... 1
1.1 TheoriesofHumor .............................. 2
1.2 HumorRecognition............................. 3
1.3 HumorGeneration ............................. 5
1.3.1 GenerativeAdversarialNetworks ..... 6
2 Related Work ....................................... 8
3 Data .................................................... 11
4 Method ............................................... 14
4.1 HumorRecognition............................ 14
4.1.1 CNN ............................... ................15
4.1.2 ModelSetting.................................. 15
4.1.3 HighwayLayer................................. 16
4.2 HumorGeneration ............................ 16
4.2.1 GenerativeAdversarialNetworks ..... 17
4.2.2 AComplementarySequenceGAN.... 18
4.2.3 Model............................................. 18
4.2.4 Algorithm....................................... 20
5 Experiment.......................................... 23
5.1 HumorRecognition............................ 23
5.1.1 Discussion ..................................... 25
5.2 HumorGeneration ............................ 26
5.2.1 HumanEvaluation .......................... 29
6 Conclusion Reference ........................ 31
Reference .............................................. 32
Salvatore Attardo. Linguistic Theories of Humor. Walter de Gruyter, 1996.

Dario Bertero and Pascale Fung. A long short-term memory framework for predicting humor in dialogues. In NAACL HLT 2016, The 2016 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego California, USA, June 12-17, 2016, pages 130–135, 2016. URL http://aclweb.org/anthology/N/N16/N16-1016.pdf.

Kim Binsted, Benjamin Bergen, and Justin McKay. Pun and non-pun humour in second- language learning. In Workshop Proceedings, CHI, 2003.

Lei Chen and Chong MIn Lee. Predicting Audience’s Laughter Using Convolutional Neural Network. ArXiv e-prints:1702.02584, February 2017.

Peng-Yu Chen and Von-Wun Soo. Humor recognition using deep learning. In Pro- ceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Pa- pers), volume 2, pages 113–117, 2018.

Luke de Oliveira and Alfredo Lainez Rodrigo. Humor detection in yelp reviews. 2015.

Sepp Hochreiter and Ju ̈rgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.

Rie Johnson and Tong Zhang. Effective use of word order for text categorization with convolutional neural networks. In NAACL HLT 2015, The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, USA, May 31 - June 5, 2015, pages 103– 112, 2015. URL http://aclweb.org/anthology/N/N15/N15-1011. pdf.

Yoon Kim. Convolutional neural networks for sentence classification. In Proceed- ings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pages 1746–1751, 2014. URL http://aclweb.org/ anthology/D/D14/D14-1181.pdf.

Greg Lessard and Michael Levison. Computational modelling of linguistic humour: Tom swifties. In In ALLC/ACH Joint Annual Conference, Oxford, pages 175–178, 1992.

Rada Mihalcea and Carlo Strapparava. Making computers laugh: Investigations in au- tomatic humor recognition. In HLT/EMNLP 2005, Human Language Technology Conference and Conference on Empirical Methods in Natural Language Process- ing, Proceedings of the Conference, 6-8 October 2005, Vancouver, British Columbia, Canada, pages 531–538, 2005. URL http://aclweb.org/anthology/H/ H05/H05-1067.pdf.

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In

C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 3111–3119. Curran Associates, Inc., 2013. URL http://papers.nips.cc/paper/ 5021-distributed-representations-of-words-and-phrases-and-their-compo pdf.

Stock Oliviero and Strapparava Carlo. Laughing with hahacronym, a computational humor system. In The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, July 16-20, 2006, Boston, Massachusetts, USA, 2006.

Jeffrey Pennington, Richard Socher, and Christopher D. Manning. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Pro- cessing (EMNLP), pages 1532–1543, 2014. URL http://www.aclweb.org/ anthology/D14-1162.
Sasa Petrovic and David Matthews. Unsupervised joke generation from big data. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013, 4-9 August 2013, Sofia, Bulgaria, Volume 2: Short Pa- pers, pages 228–232, 2013. URL http://aclweb.org/anthology/P/P13/ P13-2041.pdf.

Amruta Purandare and Diane J. Litman. Humor: Prosody analysis and automatic recog- nition for f*r*i*e*n*d*s*. In EMNLP 2007, Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, 22-23 July 2006, Sydney, Australia, pages 208–215, 2006. URL http://www.aclweb.org/anthology/ W06-1625.

He Ren and Quan Yang. Neural joke generation. 2017.

Rupesh Kumar Srivastava, Klaus Greff, and Ju ̈rgen Schmidhuber. Training very deep networks. In Advances in Neural Information Processing Systems 28: Annual Con- ference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pages 2377–2385, 2015. URL http://papers. nips.cc/paper/5850-training-very-deep-networks.

Jeff Stark, Kim Binsted, and Benjamin Bergen. Disjunctor selection for one-line jokes. In Intelligent Technologies for Interactive Entertainment, First International Con- ference, INTETAIN 2005, Madonna di Campiglio, Italy, November 30 - December 2, 2005, Proceedings, pages 174–182, 2005. doi: 10.1007/11590323 18. URL https://doi.org/10.1007/11590323_18.

J Sun. ‘jieba’chinese word segmentation tool. 2012.

Julia M. Taylor and Lawrence J. Mazlack. Computationally recognizing wordplay in
jokes. In In Proceedings of CogSci 2004, 2004.

Hans Wim Tinholt and Anton Nijholt. Computational humour: Utilizing cross- reference ambiguity for conversational jokes. In Applications of Fuzzy Sets Theory, 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, July 7-10, 2007, Proceedings, pages 477–483, 2007. doi: 10.1007/978-3-540-73400-0 60. URL https://doi.org/10.1007/ 978-3-540-73400-0_60.

C. Venour. The computational generation of a class of puns. Master’s thesis, Queen’s University, 1999. URL https://ci.nii.ac.jp/naid/10030021407/ en/.

Diyi Yang, Alon Lavie, Chris Dyer, and Eduard H. Hovy. Humor recognition and humor anchor extraction. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015, pages 2367–2376, 2015. URL http://aclweb.org/anthology/D/ D15/D15-1284.pdf.
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