Abstract
We have constructed a translation system to use easily even for beginners. In order to translate Japanese into Braille, we programmed in Python and C language using MeCab, a morphological analysis engine. However, there are many exceptions in Braille grammar, and the programming has become complicated. In recent years, researches on the machine translation using a neural network are thriving, so we carried out braille translation using the technology of neural machine translation (NMT) this time. Tensorflow was used as a library of neural network. We also incorporated a transform which was a translation component in the open source library Tensor2Tensor. We used 100,000 words of Japanese as teacher data. By using NMT, the morphological engine and the complicated programming become unnecessary. Also, MNT can translate without worrying about the complex Braille grammar. Then, just by entering Japanese sentence, the Braille codes are output. There are many advantages. We describe the braille translation using NMT.
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References
Aozora, I am a cat (2018). https://www.aozora.gr.jp/cards/000148/files/789_14547.html
Francois, C.: Deep Learning with Python. Manning Publications Co., Shelter Island (2017)
Goto, I., Tanaka, H.: Detecting untranslated content for neural machine translation. In: Proceedings of the First Workshop on Neural Machine Translation, pp. 47–55 (2017)
Hayashi, O.: Illustration Japanese. Kadokawa Publishing Co. Ltd., Fujimi (1981)
Japan Braille Committee, Revised Japan braille notation (1980)
Martłn, A., et al.: Large-scale machine learning on heterogeneous systems (2015)
Masakazu, K.: What is the reason behind the significant increase in the performance of Google Transate? The Principles and Limits of Deep Learning Applied to Natural Language Processing. KDDI Research Inc., Fujimino (2017)
Mi, H., Sankaran, B., et al.: Coverage embedding models for neural machine translation. In: Proceedings of the EMNLP, pp. 955–960 (2016)
Ministry of Health Labour and Welfare, Braille download (2018). https://www.mhlw.go.jp/tenji/index.htmlm-naAncTarget2
Morohashi, T.: Great Kanji Dictionary. Taishukan Publishing Co. Ltd., Bunkyō (2000)
National association for providing facilities for the visually impaired, Braille transcription guide, 3rd edn. (2003)
National association of information service for the visually impaired persons, The first Braille translation (2002)
Nvidia, Develop, optimize and deploy GPU-accelerated apps (2018). https://developer.nvidia.com/cuda-toolkit
Shimomura, Y., et al.: Braille translation system in Japan -past and now-. In: APIEMS2017, pp. D6–24–D6–29 (2017)
Shimomura, Y., et al.: The translation system from Japanese into braille by using MeCab. In: ICMSEM, pp. 1125–1134 (2018)
Shimomura, Y., Kawabe, H., et al.: Development of a system that beginner volunteers can translate Japanese into braille. In: APIEMS2018 (2018)
Tu, Z., Lu, Z., et al.: Modeling coverage for neural machine translation. In: Proceedings of the ACL, pp. 76–85 (2016)
Tu, Z., Yang, L., et al.: Neural machine translation with reconstruction. In: Proceedings of the AAAI (2016)
Vaswani, A., Shazeer, N., et al.: Attention is all you need. Advances in Neural Information Processing Systems (2017)
Vaswani, A., Bengio, S., et al.: Tensor2tensor for neural machine translation (2018). arXiv:180307416
Yuta, T., et al.: Natural Language Processing by Deep Learning. Kodansha, New York (2017)
Acknowledgements
This work was supported by JSPS KAKENHI Grant number 17k01097. I appreciate deeply.
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Shimomura, Y., Kawabe, H., Nambo, H., Seto, S. (2020). Braille Translation System Using Neural Machine Translation Technology I - Code Conversion. In: Xu, J., Ahmed, S., Cooke, F., Duca, G. (eds) Proceedings of the Thirteenth International Conference on Management Science and Engineering Management. ICMSEM 2019. Advances in Intelligent Systems and Computing, vol 1001. Springer, Cham. https://doi.org/10.1007/978-3-030-21248-3_25
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