Acknowledgement
이 논문은 국방과학연구소의 지원을 받아 수행된 연구임(UD190025FD).
References
- T. H. Jeon, H. S. Na, J. H. Ahn, and D. H. Im, "Pre-processing and implementation for intelligent imagery interpretation system," Proceedings of the Korea Information Processing Society Conference, Vol.28, pp.305-307, 2021.
- Y. D. Kim and H. J. Gwon, "A study on defense command and control system AI application," Korea Information Processing Society Review, Vol.24, No.1, pp.13-18, 2017.
- I. Sutskever, O. Vinyals, and V. L. Quoc, "Sequence to sequence learning with neural networks," In: Advances in neural Information Processing Systems, pp.3104-3112, 2014.
- M. Zhang, Z. Li, G. Fu, and M. Zhang, "Syntax-enhanced neural machine translation with syntax-aware word representations," Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol.1 (Long and Short Papers), 2019.
- K. Palasundram, N. M. Sharef, N. Nasharuddin, K. Kasmiran, and A. Azman "Sequence to sequence model performance for education chatbot," International Journal of Emerging Technologies in Learning (iJET), Vol.14, No.24, pp.56-68, 2019.
- K. Qian and Z. Yu. "Domain adaptive dialog generation via meta learning," Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019.
- D. Bahdanau, C. Kyunghyun, and Y. Bengio, "Neural machine translation by jointly learning to align and translate," 3rd International Conference on Learning Representations, ICLR 2015.
- S. Wiseman and A. M. Rush. "Sequence -to-sequence learning as beam-search optimization," Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016.
- pyhwp Documentation 2013. [Internet], https://pythonhosted.org/pyhwp/ko/ (accessed August 2, 2021.)
- P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, "Enriching word vectors with subword information," Transactions of the Association for Computational Linguistics, Vol.5, pp.135-146, 2017. https://doi.org/10.1162/tacl_a_00051
- E. J. Park and S. Z. Cho, "KoNLPy: Korean natural language processing in Python," Annual Conference on Human and Language Technology, pp.133-136, 2014.
- Comparison of Korean stemming analyzer performance (2018). [Internet], https://iostream.tistory.com/144 (accessed August 2, 2021)
- T. Mikolov, K. Chen, G Corrado, and J. Dean, "Efficient estimation of word representations in vector space," arXiv preprint arXiv:1301.3781, 2013.
- J. Pennington, R. Socher, and C. D. Manning, "Glove: Global vectors for word representation," Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp.1532-1543, 2014.
- Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin, "A neural probabilistic language model," Journal of Machine Learning Research, Vol.3, 1137-1155, 2003.
- T. Mikolov, M. Karafiat, L. Burget, J. Cernocky, and S. Khudanpur, "Recurrent neural network based language model," In Eleventh Annual Conference of the International Speech Communication Association, Vol.9, pp.1045-1048, 2010.
- Y. Bengio, P. Simard, and P. Frasconi, "Learning long-term dependencies with gradient descent is difficult." IEEE Transactions on Neural Networks, Vol.5, No.2, pp.157-166, 1994. https://doi.org/10.1109/72.279181
- S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, Vol.9, No.8, pp.1735-1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735
- J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, "Empirical evaluation of gated recurrent neural networks on sequence modeling." NIPS 2014 Workshop on Deep Learning, Dec. 2014.
- S. Mangal, P. Joshi, and R. Modak, "Lstm vs. gru vs. bidirectional rnn for script generation," arXiv preprint arXiv: 1908.04332, 2019.
- K. Cho, "Noisy parallel approximate decoding for conditional recurrent language model," arXiv preprint arXiv:1605.03835, 2016.