Estimating and Controlling the Appropriate Number of Output Sentences in Neural Machine Translation for Japanese–English News Translation

Author:

Ito Hitoshi1,Kinugawa Kazutaka1,Mino Hideya1,Goto Isao1,Yamada Ichiro1

Affiliation:

1. NHK放送技術研究所

Publisher

Institute of Image Information and Television Engineers

Subject

Electrical and Electronic Engineering,Computer Science Applications,Media Technology

Reference12 articles.

1. 1) Sweta Agrawal and Marine Carpuat. Controlling text complexity in neural machine translation. In Proceedings of EMNLP-IJCNLP 2019, pp. 1549-1564, November 2019.

2. 2) Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, and Matt Post. The sockeye neural machine translation toolkit at AMTA 2018. In Proceedings of AMTA 2018, pp. 200-207, March 2018.

3. 3) Shaohui Kuang and Deyi Xiong. Automatic long sentence segmentation for neural machine translation. In Proceedings of Natural Language Understanding and Intelligent Applications, pp. 162-174, 2016.

4. 4) Kelly Marchisio, Jialiang Guo, Cheng-I Lai, and Philipp Koehn. Controlling the reading level of machine translation output. In Proceedings of Machine Translation Summit XVII, pp. 193-203, August 2019.

5. 5) Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of ACL 2002, pp. 311-318, July 2002.

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