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で紹介した深層学習技術やそれらをRaNNC[12]で更に大規模にしたより高品質な言語モデルを構築し、また、これまでに構築した言語リソース等も活用して、より興味深く、かつ、社会で役立つシステムの研究開発に取り組んでいきたいと考えている。参考文献】【1A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” CoRR, vol.abs/1706.03762, 2017.2J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” Pro-ceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech-nologies (NAACL-HLT 2019), pp.4171–4186, 2019.3大竹 清敬, “社会知コミュニケーション技術の概要,” 情報通信研究機構研究報告,本特集号,3-1, 2022.4T.B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D.M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. 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