Re-Transformer: A Self-Attention Based Model for Machine Translation

Author:

Liu Huey-Ing,Chen Wei-Lin

Publisher

Elsevier BV

Subject

General Engineering

Reference22 articles.

1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . & Polosukhin, I. (2017, December). Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 6000-6010).

2. Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., . & Dean, J. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.

3. Long short-term memory;Hochreiter;Neural computation,1997

4. V Xu, H., van Genabith, J., Xiong, D., & Liu, Q. (2020). Analyzing Word Translation of Transformer Layers. arXiv preprint arXiv:2003.09586.

5. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.

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