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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 陳信希(Hsin-Hsi Chen) | |
dc.contributor.author | Jian-Fu Lin | en |
dc.contributor.author | 林建甫 | zh_TW |
dc.date.accessioned | 2021-05-20T00:54:25Z | - |
dc.date.available | 2020-07-27 | |
dc.date.available | 2021-05-20T00:54:25Z | - |
dc.date.copyright | 2020-07-27 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-07-22 | |
dc.identifier.citation | J. Chung, C. Gulcehre, K. Cho, and Y. Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014, 2014. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Comput., 9(8):1735–1780, Nov. 1997. X. Hua, Z. Hu, and L. Wang. Argument generation with retrieval, planning, and realization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2661–2672, 2019. X. Hua and L. Wang. Neural argument generation augmented with externally retrieved evidence. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 219–230, Melbourne, Australia, July 2018. Association for Computational Linguistics. X.Hua and L.Wang. Sentence-level content planning and style specific cation for neural text generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, Hong Kong, China, 2019. Association for Computational Linguistics. B. Lavoie and O. Rainbow. A fast and portable realizer for text generation systems. In Fifth Conference on Applied Natural Language Processing, pages 265–268, Washington, DC, USA, Mar. 1997. Association for Computational Linguistics. D. T. Le, C.-T. Nguyen, and K. A. Nguyen. Dave the debater: a retrieval-based and generative argumentative dialogue agent. In Proceedings of the 5th Workshop on Argument Mining, pages 121–130, Brussels, Belgium, Nov. 2018. Association for Computational Linguistics. R.Levy, B.Bogin, S.Gretz, R.Aharonov, and N.Slonim. Towards an argumentative content search engine using weak supervision. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2066–2081, Santa Fe, New Mexico, USA, Aug. 2018. Association for Computational Linguistics. J. Li, M. Galley, C. Brockett, J. Gao, and B. Dolan. A diversity-promoting objective function for neural conversation models. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 110–119, San Diego, California, June 2016. Association for Computational Linguistics. C.-Y. Lin. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74–81, Barcelona, Spain, July 2004. Association for Computational Linguistics. J.-F. Lin, K. Y. Huang, H.-H. Huang, and H.-H. Chen. Lexicon guided attentive neural network model for argument mining. In Proceedings of the 6th Workshop on Argument Mining, pages 67–73, Florence, Italy, Aug. 2019. Association for Computational Linguistics. J. Lu, C. Zhang, Z. Xie, G. Ling, T. C. Zhou, and Z. Xu. Constructing interpretive spatio-temporal features for multi-turn responses selection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 44– 50, Florence, Italy, July 2019. Association for Computational Linguistics. K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu. Bleu: A method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL ’02, page 311–318, USA, 2002. Association for Computational Linguistics. J. Pennington, R. Socher, and C. Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543, Doha, Qatar, Oct. 2014. Association for Computational Linguistics. G. Rakshit, K. K. Bowden, L. Reed, A. Misra, and M. Walker. Debbie, the debate bot of the future. arXiv preprint arXiv:1709.03167, 2017. C. Reed, D. Long, and M. Fox. An architecture for argumentative dialogue planning. In International Conference on Formal and Applied Practical Reasoning, pages 555–566. Springer, 1996. N. Reimers, B. Schiller, T. Beck, J. Daxenberger, C. Stab, and I. Gurevych. Classification and clustering of arguments with contextualized word embedding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 567–578, Florence, Italy, July 2019. Association for Computational Linguistics. A. See, P. J. Liu, and C. D. Manning. Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1073–1083, 2017. I. Serban, A. Sordoni, Y. Bengio, A. Courville, and J. Pineau. Building end-to-end dialogue systems using generative hierarchical neural network models, 2016. X. Shen, H. Su, W. Li, and D. Klakow. NEXUS network: Connecting the preceding and the following in dialogue generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4316–4327, Brussels, Belgium, Oct.-Nov. 2018. Association for Computational Linguistics. H. Su, X. Shen, R. Zhang, F. Sun, P. Hu, C. Niu, and J. Zhou. Improving multi-turn dialogue modeling with utterance ReWriter. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 22–31, Florence, Italy, July 2019. Association for Computational Linguistics. I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 3104–3112. Curran Associates, Inc., 2014. A.Vaswani, N.Shazeer, N.Parmar, J.Uszkoreit, L.Jones, A.N.Gomez, L.u.Kaiser, and I. Polosukhin. Attention is all you need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 5998–6008. Curran Associates, Inc., 2017. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8436 | - |
dc.description.abstract | 反論點生成是自然語言處理中非常具挑戰性的研究領域,它可能同時牽涉到許多子問題,例如論點探勘、自然語言生成、自然語言理解甚至資訊檢索。截至目前為止,關於反論點生成的研究只有探討單一來回情境下的生成,也就是只給定一段含有多個論點的論述並生成反論點。然而,在現實的辯論當中,一個結辯通常是透過一連串的來回討論而來,因此,一個生成反論點的模型應該需要具備組織理解多個來回之辯論歷程的能力。這篇論文有兩個主要的貢獻。首先,這是第一篇將辯論歷程引入反論點生成的文章,接著,我們建立了一個大規模的資料集、用以訓練反論點的生成模型。為了能更深入了解辯論歷程對於反論點生成的重要性,我們用數個不同的模型來做實驗,實驗結果顯示當引入辯論歷程後,模型能夠生成更加適切的反論點。 | zh_TW |
dc.description.abstract | Counter-argument generation is one of the most challenging problems in natural language processing as it involves many sub-problems like argument mining (AM), natural language generation (NLG), language understanding, or even information retrieval (IR). To date, researches on counter-argument generation only address the scenario of single-turn debate, that is, they generate counter-arguments according to one statement of someone's viewpoints. Nevertheless, in real-world debating, an argumentative conclusion usually comes along with multiple turns of discussion. Thus, an argument generation system should have the capability to model multi-turn discussion history. This thesis has two main contributions. First, this research is the first one exploring the task of counter-argument generation with multi-turn debating history context. Second, we construct a large-scale dataset which contains around 800k counter-arguments for training the generator. To further investigate the importance of debating history, we experiment with different models. The result shows that by incorporating the information of debating history, the model can generate more appropriate counter-arguments. | en |
dc.description.provenance | Made available in DSpace on 2021-05-20T00:54:25Z (GMT). No. of bitstreams: 1 U0001-1807202011412600.pdf: 780830 bytes, checksum: 80875dcb389b23c2a8e65de89dca30f5 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 誌謝 ii 摘要 iii Abstract iv 1 Introduction 1 2 Background 4 2.1 Argument Mining 4 2.2 Natural Language Generation 5 2.2.1 Sequence to Sequence Neural Networks 5 2.2.2 Beam Search 7 2.3 Metrics 8 2.3.1 BLEU 8 2.3.2 ROUGE 9 3 Related Works 11 3.1 Argument Generation 11 3.2 Conversation History Modeling 12 4 Corpus Construction 14 4.1 Data Collection 14 4.2 Domain Specifying and Data Preprocessing 15 4.3 External Evidence Retrieval 17 4.3.1 External Evidence Collection and Indexing 18 4.3.2 Query Formulation 18 4.4 Keyphrases Extraction 19 4.5 Sentence Style Labeling 19 5 Method 21 5.1 Problem Formalization 21 5.2 Input Encoding 22 5.3 Content Selection 23 5.4 Style Planing 25 5.5 Argument Generation 25 6 Experiments 28 6.1 Dataset Overview 28 6.2 Experimental Setup 30 6.2.1 Single-turn Model 31 6.2.2 Multi-turn Model 32 6.2.3 Multi-turn Model with Speaker Embedding 33 7 Results 35 7.1 Automatic Evaluation 35 7.1.1 Content Diversity 36 7.2 Human Evaluation 37 7.2.1 Annotation Setup 38 7.2.2 Result 39 8 Discussion 42 8.1 Effect of Speaker Embedding 42 8.2 Sample Generated Arguments 43 9 Conclusion 45 Bibliography 47 | |
dc.language.iso | en | |
dc.title | 基於辯論歷程之反論點生成 | zh_TW |
dc.title | Counter-argument generation with debating history | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 鄭卜壬(Pu-Jen Cheng),蔡銘峰(Ming-Feng Tsai),張嘉惠(Chia-Hui Chang) | |
dc.subject.keyword | 自然語言生成,論點探勘,論點生成, | zh_TW |
dc.subject.keyword | Natural Language Generation,Argument Mining,Argument Generation, | en |
dc.relation.page | 50 | |
dc.identifier.doi | 10.6342/NTU202001616 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2020-07-22 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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