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We propose a new training objective named order-agnostic cross entropy (OaXE) for fully non-autoregressive translation (NAT) models.
The hungarian method for the assignment problem
Kuhn, H. W · 1955
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
Earlier work this paper cites.
Exploiting probabilistic independence for permutations
Huang, J., Guestrin, C., Jiang, X., and Guibas, L · 2009
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
Sequence-level knowledge distillation
Kim, Y. and Rush, A. M · 2016
Earlier work this paper cites.
Sequence Level Training with Recurrent Neural Networks
Ranzato, M., Chopra, S., Auli, M., and Zaremba, W · 2016
Earlier work this paper cites.
Neural machine translation of rare words with subword units
Sennrich, R., Haddow, B., and Birch, A · 2016
Earlier work this paper cites.
Minimum risk training for neural machine translation
Shen, S., Cheng, Y., He, Z., He, W., Wu, H., Sun, M., and Liu, Y · 2016
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Earlier work this paper cites.
Non-autoregressive neural machine translation
Gu, J., Bradbury, J., Xiong, C., Li, V. O., and Socher, R · 2018
Earlier work this paper cites.
Deterministic non-autoregressive neural sequence modeling by iterative refinement
Lee, J., Mansimov, E., and Cho, K · 2018
Earlier work this paper cites.
End-to-end non-autoregressive neural machine translation with connectionist temporal classification
Libovický, J. and Helcl, J · 2018
Earlier work this paper cites.
Bag-of-words as target for neural machine translation
Ma, S., Sun, X., Wang, Y., and Lin, J · 2018
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Analyzing uncertainty in neural machine translation
Ott, M., Auli, M., Grangier, D., and Ranzato, M · 2018
Cited alongside, same era.
A call for clarity in reporting bleu scores
Post, M · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
Mask-predict: Parallel decoding of conditional masked language models
Ghazvininejad, M., Levy, O., Liu, Y., and Zettloyer, L · 2019
Cited alongside, same era.
Levenshtein transformer
Gu, J., Wang, C., and Zhao, J · 2019
Cited alongside, same era.
Improved natural language generation via loss truncation
Kang, D. and Hashimoto, T · 2020
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Non-autoregressive machine translation with disentangled context transformer
Kasai, J., Cross, J., Ghazvininejad, M., and Gu, J · 2020
Later among the works it cites.
Inference strategies for sequence generation with conditional masking
Kreutzer, J., Foster, G., and Cherry, C · 2020
Later among the works it cites.
Glancing transformer for non-autoregressive neural machine translation
Qian, L., Zhou, H., Bao, Y., Wang, M., Qiu, L., Zhang, W., Yu, Y., and Li, L · 2020
Later among the works it cites.
A study of non-autoregressive model for sequence generation
Ren, Y., Liu, J., Tan, X., Zhao, Z., Zhao, S., and Liu, T.-Y · 2020
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Non-autoregressive machine translation with latent alignments
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Neural machine translation with adequacy-oriented learning
Kong, X., Tu, Z., Shi, S., Hovy, E., and Zhang, T · 2019
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Hint-based training for non-autoregressive machine translation
Li, Z., Lin, Z., He, D., Tian, F., Qin, T., Wang, L., and Liu, T · 2019
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Fast block distributed cuda implementation of the hungarian algorithm
Lopes, P. A., Yadav, S. S., Ilic, A., and Patra, S. K · 2019
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Flowseq: Non-autoregressive conditional sequence generation with generative flow
Ma, X., Zhou, C., Li, X., Neubig, G., and Hovy, E · 2019
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Fast structured decoding for sequence models
Sun, Z., Li, Z., Wang, H., Lin, Z., He, D., and Deng, Z.-H · 2019
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Aligned cross entropy for non-autoregressive machine translation
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Minimizing the bag-of-ngrams difference for non-autoregressive neural machine translation
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Latent-variable non-autoregressive neural machine translation with deterministic inference using a delta posterior
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An em approach to non-autoregressive conditional sequence generation
Sun, Z. and Yang, Y · 2020
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Understanding knowledge distillation in non-autoregressive machine translation
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Deep encoder, shallow decoder: Reevaluating non-autoregressive machine translation
Kasai, J., Pappas, N., Peng, H., Cross, J., and Smith, N · 2021
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