$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

I-QANet: 그래프 컨볼루션 네트워크를 활용한 향상된 기계독해
I-QANet: Improved Machine Reading Comprehension using Graph Convolutional Networks 원문보기

멀티미디어학회논문지 = Journal of Korea Multimedia Society, v.25 no.11, 2022년, pp.1643 - 1652  

김정훈 (Interdisciplinary Program in IT-Bio Convergence System, Sunchon National University) ,  김준영 (Interdisciplinary Program in IT-Bio Convergence System, Sunchon National University) ,  박준 (Interdisciplinary Program in IT-Bio Convergence System, Sunchon National University) ,  박성욱 (Interdisciplinary Program in IT-Bio Convergence System, Sunchon National University) ,  정세훈 (Dept. of Computer Engineering, Sunchon National University) ,  심춘보 (Dept. of Artificial Intelligence Engineering, Sunchon National University)

Abstract ▼ AI-Helper 아이콘AI-Helper

Most of the existing machine reading research has used Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) algorithms as networks. Among them, RNN was slow in training, and Question Answering Network (QANet) was announced to improve training speed. QANet is a model composed of CNN ...

주제어

#Machine Reading Comprehension   #QANet   #Graph Convolutional Networks  

참고문헌 (25)

  1. S. Liu, X. Zhang, S. Zhang, H. Wang, and W. Zhang, "Neural Machine Reading Comprehension: Methods and Trends," Applied Sciences, Vol. 9, No. 18, pp. 3698, 2019. 

    상세보기 crossref

  2. C. Zeng, S. Li, Q. Li, J. Hu, and J. Hu, "A Survey on Machine Reading Comprehension -Tasks, Evaluation Metrics and Benchmark Datasets," Applied Sciences, Vol. 10, No. 21, pp. 7640, 2020. 

    상세보기 crossref

  3. Z. Zhang, H. Zhao, and R. Wang, "Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond," arXiv Preprint, arXiv:2005.06249, 2020. 

  4. A.W. Yu, D. Dohan, M. Luong, R. Zhao, K. Chen, M. Norouzi, et al., "QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension," Proceedings of International Conference on Learning Representations 2018: the 6th International Conference on Learning Representations, 2018. 

  5. W.L. Hamilton, R. Ying, and J. Leskovec, "Representation Learning on Graphs: Methods and Applications," Institute of Electrical and Electronics Engineers Data Engineering Bulletin, Vol. 40, No. 3, pp. 52-74, 2017. 

  6. J. Zhoua, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, et al., "Graph Neural Networks: A Review of Methods and Applications," AI Open, Vol. 1, pp. 57-81, 2020. 

    상세보기 crossref

  7. S. Wang and J. Jiang, "Learning Natural Language Inference with LSTM," Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1442-1451, 2016. 

  8. W. Shuohang and J. Jing, "Machine Comprehension Using Match-LSTM and Answer Pointer," arXiv Preprint, arXiv:1608.07905, 2016. 

  9. Y. Cui, Z. Chen, S. Wei, S. Wang, T. Liu, and G. Hu, "Attention-over-Attention Neural Networks for Reading Comprehension," Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vol. 1, pp. 593-602, 2017. 

  10. W. Wang, N. Yang, F. Wei, B. Chang, and M. Zhou, "Gated Self-Matching Networks for Reading Comprehension and Question Answering," Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vol. 1, pp. 189-198, 2017. 

  11. C. Tan, F. Wei, N. Yang, W. Lv, and M. Zhou, "S-Net: From Answer Extraction to Answer Generation for Machine Reading ComprehenSion," arXiv Preprint, arXiv:1706.04815, 2017. 

  12. M. Seo, A. Kembhavi, A. Farhadi, and H. Hajishirzi, "Bidirectional Attention Flow for Machine Comprehension," International Conference on Learning Representations, 2016. 

  13. C. Xiong, V. Zhong, and R. Socher, "Dynamic Coattention Networks for Question Answering," arXiv Preprint, arXiv:1611.01604, 2016. 

  14. A. Chaturvedi, O. Pandit, and U. Garain, "CNN for Text-Based Multiple Choice Question Answering," Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Vol. 4, pp. 272-277, 2018. 

  15. Z. Chen, Y. Cui, W. Ma, S. Wang, and G. Hu, "Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions," Association for the Advancement of Artificial Intelligence, Vol. 33, No. 1, pp. 6276-6283, 2019. 

  16. P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, "Graph Attention Networks," International Conference on Learning Representations, 2018. 

  17. L. Yao, C. Mao, and Y. Luo, "Graph Convolutional Networks for Text Classification," Proceedings of the Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence, Vol. 33, pp. 7370-7377, 2019. 

  18. K. Xu, W. Hu, J. Leskovec, and S. Jegelka, "How Powerful are Graph Neural Networks," Proceedings of International Conference on Learning Representations, pp. 1-17, 2019. 

  19. Q. Ran, Y. Lin, P. Li, J. Zhou, and Z. Liu, "NumNet: Machine Reading Comprehension with Numerical Reasoning," Conference on Empirical Methods in Natural Language Processing & International Joint Conference on Natural Language Processing, pp. 2474-2484, 2019. 

  20. J. Zhang, H. Zhang, L. Sun, and C. Xia, "Graph-BERT: Only Attention is Needed for Learning Graph Representations," arXiv Preprint, arXiv:2001.05140, 2020. 

  21. S.W. Park and D.Y. Kim, "Comparison of Image Classification Performance in Convolutional Neural Network according to Transfer Learning," Journal of Korea Multimedia Society, Vol. 21, No. 12, pp. 1387-1395, 2018. 

    원문보기 상세보기 crossref

  22. R.K. Srivastava, K. Greff, and J. Schmidhuber, "Highway Networks," Clinical Orthopaedics and Related Research, abs/1505.00387, 2015. 

  23. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, et al., "Attention is All You Need," Advances in Neural Information Processing Systems, 2017. 

  24. P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, "SQuAD: 100,000+ Questions for Machine Comprehension of Text," Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2383-2392, 2016. 

  25. S. Lim, M. Kim, and J. Lee, "KorQuAD: Korean QA Dataset for Machine Comprehension," Proceedings of the Korean Information Science Society Conference, pp. 539-541, 2018. 

저자의 다른 논문 :

관련 콘텐츠

오픈액세스(OA) 유형

BRONZE

출판사/학술단체 등이 한시적으로 특별한 프로모션 또는 일정기간 경과 후 접근을 허용하여, 출판사/학술단체 등의 사이트에서 이용 가능한 논문

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로