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研究生: 許瑋倫
Hsu, Wei-Lun
論文名稱: 基於檢索的中文幽默對話系統建置與評估
Implementation and Evaluation of Chinese Humor Retrieval-based Dialog System
指導教授: 曾元顯
Tseng, Yuen-Hsien
學位類別: 碩士
Master
系所名稱: 圖書資訊學研究所
Graduate Institute of Library and Information Studies
論文出版年: 2020
畢業學年度: 108
語文別: 中文
論文頁數: 74
中文關鍵詞: 計算幽默中文幽默對話幽默語料對話系統破冰機器人
英文關鍵詞: Computational Humor, Chinese Humorous Dialogue, Humor Corpus, Dialogue System, Icebreaker Chatbot
DOI URL: http://doi.org/10.6345/NTNU202000810
論文種類: 學術論文
相關次數: 點閱:98下載:28
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  • 幽默對話是人際溝通中一項重要的元素,也是人機互動的重要進程之一。本研究透過實作中文幽默對話系統—「破冰機器人」。設置情境,讓使用者查詢相關的笑話並說出,以打破尷尬、僵硬的氣氛並評估其成效。透過系統開發研究法的循環步驟,經過回饋後加入Word2Vec的查詢擴展、關鍵字查詢提示,以及好笑笑話的隨機推薦等功能,讓使用者找不到笑話的比例從25.4%降低到16.7%,而系統達到的破冰效果從27.9%提升到39.9%。可以知道系統優化後確實可以有效的提升使用者的使用率以及破冰效果。實驗後進行語料庫的一致性評估,研究發現:
    1. 破冰機器人確實可達到其成效。
    2. 語料庫中的好笑程度與使用者的認知接近一致性的臨界值:使用者認為越好笑的笑話,越能達到破冰效果。
    綜合而言,本研究的貢獻,不僅進行了幽默語料庫的應用,也建置中文幽默對話系統。並且在研究過程與結果中,提供了實證經驗與意涵:笑話語料的豐富程度與品質(收集更多笑話並標註好笑程度)、以及普遍使用者已經習慣推薦功能大於自己查詢的趨勢。後續的各類對話系統,建議應運用類似的推薦功能,以符合現今使用者的習慣與期待。

    Humorous dialogue is an important element in interpersonal communication and is also one of the important processes for human-computer interaction. The purpose of this research is to develop related technologies, implement a retrieval-based "icebreaker robot" system which allows users to find relevant jokes for use in relaxing an unduly formal atmosphere when interacting with people, and evaluate its effectiveness. Through the iterative steps of the information system development methodology, query expansion based on Word2Vec technology, frequent keyword prompts, and random recommendation of good jokes are added after user feedback. The results are that the proportion of user queries that fail to find jokes is reduced from 25.4% to 8.0% and that the icebreaker effect achieved has been increased from 25.9% to 40.9%. System optimization can accurately increase the usage rate and effectiveness. By the conformance assessment, get the conclusion of the research below:
    1. Icebreaker robot has an effect on relaxing an unduly formal atmosphere.
    2. The humor level in the corpus and users cognition are conformance but close to critical value:the funnier jokes that user thinks, better the effect of icebreaker can be achieved.
    Empirical experience and implications of this study include: the richness and quality of joke corpus (collecting more jokes and identifying their humor level) and the automatic recommendation relative to passive search are important R & D efforts to improve the effectiveness of such services.

    第一章 緒論 1 第一節 研究動機 1 第二節 研究目的與問題 3 第三節 名詞解釋 4 第二章 文獻探討 5 第一節 文字對話系統與幽默計算 5 第二節 幽默語料庫 8 第三節 幽默對話系統與評估方法 11 第三章 研究方法與實施 18 第一節 研究方法 18 第二節 研究範圍與限制 20 第三節 研究架構 21 第四節 研究實施與步驟 25 第四章 中文幽默對話系統建置與評估 28 第一節 使用語料 28 第二節 檢索比對生成模組 30 第三節 建置系統 35 第四節 實驗與評估 38 第五章 結論與後續研究 48 第一節 結論 48 第二節 後續研究 49 參考文獻 51 附錄 56

    任璐、楊亮、徐琳宏、樊小超、刁宇峰、林鴻飛(2018)。中文笑話語料庫的構建與應用。中文信息學報,32(7),20-29。
    周平(2011)。幽默的心理緣起與社會緣起一種關係-過程的笑話社會學取徑。國科會計畫(編號:NSC99-2410-H343-025-MY2)。
    洪淑芬(2013)。圖書資訊學研究中的科學研究方法:以系統開發研究法為例。大學圖書館,17(1),107-121。doi:10.6146/univj.17-1.06
    陳淑蓉、陳學志(2005)。幽默感的定義與測量:多向度幽默感量表之編製。應用心理研究,(26),167-187。
    國立中央大學自然語言處理實驗室—中文相似詞搜尋(2019)。取自:http://ai.ee.ncu.edu.tw/embeddingsearch
    曾元顯(2017)。【中文幽默對話系統之研發】。科技部計畫(編號:MOST 107-2221-E-003-014-MY2)。
    曾元顯、許瑋倫、吳玟萱、古怡巧、陳學志(2020)。基於檢索方法的中文幽默對話系統之建置應用與評估。圖書資訊學刊。取自:https://jlis.lis.ntu.edu.tw/html/index.html。
    鄭昭明、陳學志、詹雨臻、蘇雅靜、曾千芝(2013)。台灣地區華人情緒與相關心理生理資料庫─中文笑話評定常模。中華心理學刊,55(4),555-569。doi:10.6129/CJP.20121026
    Augello, A., Saccone, G., Gaglio, S., & Pilato, G. (2008). Humorist Bot: Bringing Computational Humour in a Chat-Bot System. Paper presented at the International Conference on Complex, Intelligent and Software Intensive Systems.
    Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189. doi:10.1016/j.chb.2018.03.051.
    Blinov, V., Mishchenko, K., Bolotova, V., & Braslavski, P. (2017). A Pinch of Humor for Short-Text Conversation: An Information Retrieval Approach. Paper presented at the Experimental IR Meets Multilinguality, Multimodality, and Interaction: 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11–14, 2017, Proceedings, Cham. https://doi.org/10.1007/978-3-319-65813-1_1
    Yi-Ciao Gu, Yuen-Hsien Tseng, Wei-Lun Hsu, Wun-Syuan Wu and Hsueh-Chih Chen (2019). Development and Classification of a Chinese Humor Corpus. Paper presented at the 20th International Conference on Computational Linguistics and Intelligent Text Processing, La Rochelle, France.
    icebreaker. (2019). Cambridge Advanced Learner's Dictionary. Retrieved from https://dictionary.cambridge.org/zht/dictionary/english-chinese-traditional/icebreaker
    Ji, Z., Lu, Z., & Li, H. (2014). An Information Retrieval Approach to Short Text Conversation. arXiv:1408.6988.
    Le, Q. V., & Mikolov, T. (2014). Distributed representations of sentences and documents. arXiv preprint arXiv:1405.4053.
    Mihalcea, R., & Strapparava, C. (2006a). Learning to Laugh (Automatically): Computational Models for Humor Recognition. Computational Intelligence, 22(2), 126-142.
    Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
    Newyear, D., & McNeal, M. (Producer). (2014). Extending Library Services with AI Conversational Agents. Retrieved from http://connect.ala.org/files/AI_Conversational_Agents.pptx
    Nunamaker, J. F., Chen, M., & Purdin, T. D. M. (1990). Systems Development in Information Systems Research. Journal of Management Information Systems, 7(3), 89-106. doi:10.1080/07421222.1990.11517898
    Potash, P., Romanov, A., & Rumshisky, A. (2017). SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor. Paper presented at the 11th International Workshop on Semantic Evaluations, Vancouver, Canada.
    Provine, R. R. (2001). Laughter: A Scientific Investigation. London, UK: Penguin Books.
    Radziwill, N. M., & Benton, M. C. (2017). Evaluating Quality of Chatbots and Intelligent Conversational Agents. Software Quality Professional, 19(3), 25. Retrieved from https://arxiv.org/abs/1704.04579
    Sjobergh, J., & Araki, K. (2009). A Very Modular Humor Enabled Chat-Bot for Japanese. Paper presented at the Conference of the Pacific Association for Computational Linguistics, Sapporo, Japan.
    Strick, M., van Baaren, R. B., Holland, R. W., & van Knippenberg, A. (2011). Humor in advertisements enhances product liking by mere association.Psychology of Popular Media Culture, 1(S), 16–31.https://doi.org/10.1037/2160-4134.1.S.16
    Sure, Y., & Studer, R. (2002). On-to-knowledge: Content-driven knowledge management tools through evolving ontologies (Methodology -- Final Version) (EU-IST Project IST-1999-10132). Institute AIFB, University of Karlsruhe. —(2004). A methodology for ontology-based knowledge management. In J. Davies, D. Fensel, & F. V. Harmelen (Eds.), Towards the semantic web: Ontology-driven knowledge management (pp. 33-46). London: John Wiley & Sons.
    Tseng, Y.-H., & Teahan, W. J. (2004, July 25 - 29). Verifying a Chinese Collection for Text Categorization. Paper presented at the 27th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '04, Sheffield, U.K.
    Vasconcelos, M., Candello, H., Pinhanez, C., & Santos, T. d. (2017). Bottester: Testing Conversational Systems with Simulated Users. Paper presented at the Proceedings of the XVI Brazilian Symposium on Human Factors in Computing Systems, Joinville, Brazil. Retrieved from https://dl.acm.org/citation.cfm?id=3160584
    Wang, H., Zhang, Q., Ip, M., & Lau, J. T. F. (2018). Social Media–based Conversational Agents for Health Management and Interventions. Computer, 51(8), 26-33. doi:10.1109/MC.2018.3191249. Retrieved from https://ieeexplore.ieee.org/document/8436412
    Wen, T.-H., Gasic, M., Kim, D., Mrksic, N., Su, P.-H., Vandyke, D., & Young, S. (2015). Stochastic Language Generation in Dialogue Using Recurrent Neural Networks with Convolutional Sentence Reranking. arXiv:1508.01755.
    Wen, T.-H., Gasic, M., Mrksic, N., Su, P.-H., Vandyke, D., & Young, S. (2015). Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems. arXiv preprint arXiv:1508.01745.
    Yang, D., Lavie, A., Dyer, C., & Hovy, E. (2015). Humor Recognition and Humor Anchor Extraction. Paper presented at the Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.
    Zhang, R., & Liu, N. (2014). Recognizing Humor on Twitter. Paper presented at the 23rd ACM International Conference on Information and Knowledge Management, Shanghai, China.

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