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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳信希 | |
| dc.contributor.author | Hung-Kuo Liu | en |
| dc.contributor.author | 劉宏國 | zh_TW |
| dc.date.accessioned | 2021-06-17T08:43:32Z | - |
| dc.date.available | 2019-08-12 | |
| dc.date.copyright | 2019-08-12 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-07 | |
| dc.identifier.citation | Chen, Z., Cui, Y., Ma, W., Wang, S., Liu, T., & Hu, G. (2018). HFL-RC system at SemEval-2018 task 11: hybrid multi-aspects model for commonsense reading comprehension. arXiv preprint arXiv:1803.05655.
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. Cui, Y., Chen, Z., Wei, S., Wang, S., Liu, T., & Hu, G. (2016). Attention-over-attention neural networks for reading comprehension. arXiv preprint arXiv:1607.04423. González, J.-Á., Hurtado, L.-F., Segarra, E., & Pla, F. (2018). ELiRF-UPV at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. Paper presented at the Proceedings of The 12th International Workshop on Semantic Evaluation. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. Kampmann, A., Thater, S., & Pinkal, M. (2015). A Case-study of Automatic Participant Labeling. Paper presented at the GSCL. Lai, G., Xie, Q., Liu, H., Yang, Y., & Hovy, E. (2017). Race: Large-scale reading comprehension dataset from examinations. arXiv preprint arXiv:1704.04683. Miller, G. A. (1995). WordNet: a lexical database for English. Communications of the ACM, 38(11), 39-41. Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Yang, B., Betteridge, J., . . . Kisiel, B. (2018). Never-ending learning. Communications of the ACM, 61(5), 103-115. Modi, A., Anikina, T., Ostermann, S., & Pinkal, M. (2017). Inscript: Narrative texts annotated with script information. arXiv preprint arXiv:1703.05260. Ostermann, S., Modi, A., Roth, M., Thater, S., & Pinkal, M. (2018). MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge. Paper presented at the Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018). Ostermann, S., Roth, M., Modi, A., Thater, S., & Pinkal, M. (2018). SemEval-2018 Task 11: machine comprehension using commonsense knowledge. Paper presented at the Proceedings of The 12th International Workshop on Semantic Evaluation. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . Dubourg, V. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830. Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global vectors for word representation. Paper presented at the Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Rajpurkar, P., Jia, R., & Liang, P. (2018). Know What You Don't Know: Unanswerable Questions for SQuAD. arXiv preprint arXiv:1806.03822. Regneri, M., Koller, A., & Pinkal, M. (2010). Learning script knowledge with web experiments. Paper presented at the Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Reznikova, S., & Derczynski, L. (2018). IUCM at SemEval-2018 Task 11: Similar-Topic Texts as a Comprehension Knowledge Source. Paper presented at the Proceedings of The 12th International Workshop on Semantic Evaluation. Singh, P., Lin, T., Mueller, E. T., Lim, G., Perkins, T., & Zhu, W. L. (2002). Open Mind Common Sense: Knowledge acquisition from the general public. Paper presented at the OTM Confederated International Conferences' On the Move to Meaningful Internet Systems'. Speer, R., Chin, J., & Havasi, C. (2017). Conceptnet 5.5: An open multilingual graph of general knowledge. Paper presented at the Thirty-First AAAI Conference on Artificial Intelligence. Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015). Highway networks. arXiv preprint arXiv:1505.00387. Tandon, N., de Melo, G., & Weikum, G. (2017). Webchild 2.0: Fine-grained commonsense knowledge distillation. Proceedings of ACL 2017, System Demonstrations, 115-120. Wang, L., Sun, M., Zhao, W., Shen, K., & Liu, J. (2018). Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension. Paper presented at the Proceedings of The 12th International Workshop on Semantic Evaluation. Wanzare, L. D., Zarcone, A., Thater, S., & Pinkal, M. (2016). Descript: A crowdsourced corpus for the acquisition of high-quality script knowledge. Paper presented at the Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). Yang, B., & Mitchell, T. (2017). Leveraging Knowledge Bases in LSTMs for Improving Machine Reading. Paper presented at the Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Young, T., Cambria, E., Chaturvedi, I., Zhou, H., Biswas, S., & Huang, M. (2018). Augmenting end-to-end dialogue systems with commonsense knowledge. Paper presented at the Thirty-Second AAAI Conference on Artificial Intelligence. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74574 | - |
| dc.description.abstract | 近年來，在機器閱讀理解的任務中引入常識知識是廣泛被討論的研究議題。 以往大多數的研究都使用ConceptNet來推斷抽象概念，並幫助他們的模型來回答閱讀理解中的問題。然而，很少有研究採用腳本知識來改進或增強機器閱讀理解的模型。本文透過結合腳本知識對各種類型的常識進行建模，提出了一種新的機器閱讀理解的模型。而實驗結果顯示, 我們的模型在MCScript的數據集上達到了在SemEval-2018 Task 11中最佳的性能也提升了在COIN數據集的效能。 | zh_TW |
| dc.description.abstract | Introducing commonsense knowledge to the machine reading comprehension (MRC) task attracts attention in recent years. Most studies use ConceptNet to inference the abstract concepts and help their models answer the questions in reading comprehension. However, few studies employ Script knowledge to improve their MRC models. This thesis proposes a novel model for MRC by incorporating Script knowledge for modeling the various types of commonsense. Experimental results show that our model achieves the best performance on the MCScript dataset in the SemEval-2018 Task 11 and it increases the accuracy on the COIN dataset. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T08:43:32Z (GMT). No. of bitstreams: 1 ntu-108-R06922006-1.pdf: 1628070 bytes, checksum: 7280c0f10b43fa58a316a992ac71a7c6 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 1.1 Machine Reading Comprehension 1 1.2 Commonsense Knowledge 1 1.3 Motivation 2 1.4 Organization 3 Chapter 2 Related work 4 2.1 Script Knowledge 4 2.1.1 DeScript corpus 5 2.1.2 RKP corpus 5 2.1.3 OMCS Stories corpus 6 2.2 ConceptNet 6 2.3 Recent Work on Using Commonsense Knowledge 7 2.4 SemEval-2018 Task 11 8 Chapter 3 Proposed Methodology 10 3.1 Overview 10 3.2 Script Event Descriptions Retrieval 12 3.3 ConceptNet Assertion Retrieval 13 3.4 Model Architecture 15 Chapter 4 Dataset 23 4.1 MCScript 23 4.2 COIN dataset 24 4.3 Script Knowledge 25 4.4 Story Scenario 26 Chapter 5 Experiments and Analysis 27 5.1 Scenario Prediction 27 5.2 MRC main Result 27 5.3 Script Knowledge Comparison 29 5.3.1 Script knowledge experimental result 29 5.3.2 Script knowledge analysis 30 5.4 COIN Result 34 Chapter 6 Conclusion and Future Work 35 6.1 Conclusions 35 6.2 Future Work 36 References 37 | |
| dc.language.iso | en | |
| dc.title | 整合腳本知識的機器常識閱讀理解 | zh_TW |
| dc.title | Modeling Script Knowledge for Machine Commonsense Reading Comprehension | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 鄭卜壬,蔡宗翰,陳冠宇 | |
| dc.subject.keyword | 機器閱讀理解,腳本知識,常識知識, | zh_TW |
| dc.subject.keyword | Machine Reading Comprehension,Script Knowledge,Commonsense Knowledge, | en |
| dc.relation.page | 40 | |
| dc.identifier.doi | 10.6342/NTU201900781 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-07 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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