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OpenNMT is an open-source toolkit for neural machine translation (NMT).
Long short-term memory
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Moses: Open source toolkit for statistical machine translation
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cdec: A decoder, alignment, and learning framework for finite-state and context-free translation models
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Large scale distributed deep networks
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Neural Machine Translation By Jointly Learning To Align and Translate
Bahdanau, D., Cho, K., and Bengio, Y. (2014) · 2014
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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014) · 2014
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014) · 2014
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Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., and Le, Q. V. (2014) · 2014
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Chan, W., Jaitly, N., Le, Q. V., and Vinyals, O. (2015) · 2015
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Neural machine translation of rare words with subword units
Sennrich, R., Haddow, B., and Birch, A. (2015) · 2015
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Pointer networks
Vinyals, O., Fortunato, M., and Jaitly, N. (2015) · 2015
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Vinyals, O. and Le, Q. (2015) · 2015
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Abstractive sentence summarization with attentive recurrent neural networks
Chopra, S., Auli, M., and Rush, A. M. (2016) · 2016
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Systran’s pure neural machine translation system
Crego, J., Kim, J., and Senellart, J. (2016) · 2016
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Incorporating copying mechanism in sequence-to-sequence learning
Gu, J., Lu, Z., Li, H., and Li, V. O. (2016) · 2016
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From softmax to sparsemax: A sparse model of attention and multi-label classification
Martins, A. F. and Astudillo, R. F. (2016) · 2016
Convolutional sequence to sequence learning
Gehring, J., Auli, M., Grangier, D., Yarats, D., and Dauphin, Y. N. (2017) · 2017
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Effective strategies in zero-shot neural machine translation
Ha, T.-L., Niehues, J., and Waibel, A. (2017) · 2017
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Sockeye: A toolkit for neural machine translation
Hieber, F., Domhan, T., Denkowski, M., Vilar, D., Sokolov, A., Clifton, A., and Post, M. (2017) · 2017
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Kim, Y., Denton, C., Hoang, L., and Rush, A. M. (2017) · 2017
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Toward a full-scale neural machine translation in production: the booking. com use case
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Linguistic input features improve neural machine translation
Sennrich, R. and Haddow, B. (2016) · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016) · 2016
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Arabic machine transliteration using an attention-based encoder-decoder model
Ameur, M. S. H., Meziane, F., and Guessoum, A. (2017) · 2017
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Massive exploration of neural machine translation architectures
Britz, D., Goldie, A., Luong, T., and Le, Q. (2017) · 2017
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Image-to-markup generation with coarse-to-fine attention
Deng, Y., Kanervisto, A., Ling, J., and Rush, A. M. (2017) · 2017
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The webnlg challenge: Generating text from rdf data
Gardent, C., Shimorina, A., Narayan, S., and Perez-Beltrachini, L. (2017) · 2017
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Effective Approaches to Attention-based Neural Machine Translation
Luong, M.-T., Pham, H., and Manning, C. D. (2015a)
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Levin, P., Dhanuka, N., Khalil, T., Kovalev, F., and Khalilov, M. (2017) · 2017
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Coarse-to-fine attention models for document summarization
Ling, J. and Rush, A. (2017) · 2017
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Osu multimodal machine translation system report
Ma, M., Li, D., Zhao, K., and Huang, L. (2017) · 2017
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van Noord, R. and Bos, J. (2017) · 2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017) · 2017
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Challenges in data-to-document generation
Wiseman, S., Shieber, S. M., and Rush, A. M. (2017) · 2017
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Show, attend and tell: Neural image caption generation with visual attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y. (2015) · 2057
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