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Neural Machine Translation (NMT) is an open vocabulary problem.
On a test of whether one of two random variables is stochastically larger than the other
Mann, H. B. and Whitney, D. R. (1947) · 1947
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Morphological word segmentation on agglutinative languages for neural machine translation
Pan, Y., Li, X., Yang, Y., and Dong, R. (2020) · 2001
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Manual and automatic evaluation of machine translation between european languages
Koehn, P. and Monz, C. (2006) · 2006
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Fast, cheap, and creative: Evaluating translation quality using amazon’s mechanical turk
Callison-Burch, C. (2009) · 2009
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A simple, fast, and effective reparameterization of IBM model 2
Dyer, C., Chahuneau, V., and Smith, N. A. (2013) · 2013
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Continuous measurement scales in human evaluation of machine translation
Graham, Y., Baldwin, T., Moffat, A., and Zobel, J. (2013) · 2013
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Randomized significance tests in machine translation
Graham, Y., Mathur, N., and Baldwin, T. (2014) · 2014
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Morfessor 2.0: Toolkit for statistical morphological segmentation
Smit, P., Virpioja, S., Grönroos, S., and Kurimo, M. (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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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y. (2015) · 2015
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On using very large target vocabulary for neural machine translation
Jean, S., Cho, K., Memisevic, R., and Bengio, Y. (2015) · 2015
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Addressing the rare word problem in neural machine translation
Luong, T., Sutskever, I., Le, Q. V., Vinyals, O., and Zaremba, W. (2015) · 2015
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Character-based neural machine translation
Costa-jussà, M. R. and Fonollosa, J. A. R. (2016) · 2016
Cited alongside, same era.
Pointing the unknown words
Gülçehre, Ç., Ahn, S., Nallapati, R., Zhou, B., and Bengio, Y. (2016) · 2016
Cited alongside, same era.
Neural machine translation of rare words with subword units
Sennrich, R., Haddow, B., and Birch, A. (2016) · 2016
Cited alongside, same era.
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., Klingner, J., Shah, A., Johnson, M., Liu, X., Kaiser, L., Gouws, S., Kato, Y., Kudo, T., Kazawa, H., Stevens, K., Kurian, G., Patil, N., Wang, W., Young, C., Smith, J., Riesa, J., Rudnick, A., Vinyals, O., Corrado, G., Hughes, M., and Dean, J. (2016) · 2016
Cited alongside, same era.
Target-side word segmentation strategies for neural machine translation
Huck, M., Riess, S., and Fraser, A. M. (2017) · 2017
Cited alongside, same era.
The TALP-UPC machine translation systems for WMT19 news translation task: Pivoting techniques for low resource MT
Casas, N., Fonollosa, J. A. R., Escolano, C., Basta, C., and Costa-jussà, M. R. (2019) · 2019
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Investigating the effectiveness of BPE: the power of shorter sequences
Gallé, M. (2019) · 2019
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Jointly learning to align and translate with transformer models
Garg, S., Peitz, S., Nallasamy, U., and Paulik, M. (2019) · 2019
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Incorporating word and subword units in unsupervised machine translation using language model rescoring
Liu, Z., Xu, Y., Winata, G. I., and Fung, P. (2019) · 2019
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Hierarchical transfer learning architecture for low-resource neural machine translation
Luo, G., Yang, Y., Yuan, Y., Chen, Z., and Ainiwaer, A. (2019) · 2019
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017) · 2017
Cited alongside, same era.
Meaningless yet meaningful: Morphology grounded subword-level NMT
Banerjee, T. and Bhattacharyya, P. (2018) · 2018
Cited alongside, same era.
Revisiting character-based neural machine translation with capacity and compression
Cherry, C., Foster, G. F., Bapna, A., Firat, O., and Macherey, W. (2018) · 2018
Cited alongside, same era.
How much does tokenization affect neural machine translation?
Domingo, M., García-Martínez, M., Helle, A., Casacuberta, F., and Herranz, M. (2018) · 2018
Cited alongside, same era.
A pronoun test suite evaluation of the english-german MT systems at WMT 2018
Guillou, L., Hardmeier, C., Lapshinova-Koltunski, E., and Loáiciga, S. (2018) · 2018
Cited alongside, same era.
Subword regularization: Improving neural network translation models with multiple subword candidates
Kudo, T. (2018) · 2018
Cited alongside, same era.
Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Kudo, T. and Richardson, J. (2018) · 2018
Cited alongside, same era.
Facebook fair’s WMT19 news translation task submission
Ng, N., Yee, K., Baevski, A., Ott, M., Auli, M., and Edunov, S. (2019) · 2019
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Optimizing transformer for low-resource neural machine translation
Araabi, A. and Monz, C. (2020) · 2020
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Accurate word alignment induction from neural machine translation
Chen, Y., Liu, Y., Chen, G., Jiang, X., and Liu, Q. (2020) · 2020
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Dynamic programming encoding for subword segmentation in neural machine translation
He, X., Haffari, G., and Norouzi, M. (2020) · 2020
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A study of bpe-based language modeling for open vocabulary latin language OCR
Hu, W., Luo, Y., Meng, J., Qian, Z., and Huo, Q. (2020) · 2020
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Bpe-dropout: Simple and effective subword regularization
Provilkov, I., Emelianenko, D., and Voita, E. (2020) · 2020
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The FLORES-101 evaluation benchmark for low-resource and multilingual machine translation
Goyal, N., Gao, C., Chaudhary, V., Chen, P., Wenzek, G., Ju, D., Krishnan, S., Ranzato, M., Guzmán, F., and Fan, A. (2021) · 2021
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