Generic Mechanism for Reducing Repetitions in Encoder-decoder Models

Author:

Zhang Ying1,Kamigaito Hidetaka2,Aoki Tatsuya1,Takamura Hiroya3,Okumura Manabu1

Affiliation:

1. Tokyo Institute of Technology

2. NARA Institute of Science and Technology (NAIST)

3. National Institute of Advanced Industrial Science and Technology (AIST)

Publisher

Association for Natural Language Processing

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference40 articles.

1. Bahdanau, D., Cho, K. H., and Bengio, Y. (2015). “Neural Machine Translation by Jointly Learning to Align and Translate.” In 3rd International Conference on Learning Representations, ICLR 2015.

2. Bengio, Y. et al. (2009). “Learning Deep Architectures for AI.” Foundations and Trends® in Machine Learning, 2 (1), pp. 1–127.

3. Bojar, O., Buck, C., Federmann, C., Haddow, B., Koehn, P., Leveling, J., Monz, C., Pecina, P., Post, M., Saint-Amand, H., Soricut, R., Specia, L., and Tamchyna, A. (2014). “Findings of the 2014 Workshop on Statistical Machine Translation.” In Proceedings of the 9th Workshop on Statistical Machine Translation, pp. 12–58, Baltimore, Maryland, USA. Association for Computational Linguistics.

4. Brier, G. W. (1950). “Verification of Forecasts Expressed in Terms of Probability.” Monthly Weather Review, 78 (1), pp. 1–3.

5. Cettolo, M., Niehues, J., Stüker, S., Bentivogli, L., and Federico, M. (2014). “Report on the 11th IWSLT Evaluation Campaign, IWSLT 2014.” In Proceedings of the International Workshop on Spoken Language Translation, Hanoi, Vietnam, p. 57.

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