Built on

T. Sho and O. Naoaki, “Positional encoding to control output sequence length,” in Proceedings of the HLT-NAACL 2019 , 2019. [Online]. Available: http://arxiv.org/abs/1904.07418
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1904
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E. Noreen, Computer-Intensive Methods for Testing Hypotheses: An Introduction . Wiley, 1989
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K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting on association for computational linguistics . Association for Computational Linguistics, 2002, pp. 311–318
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P. Koehn, H. Hoang, A. Birch, C. Callison-Burch et al. , “Moses: Open source toolkit for statistical machine translation,” in Proc. of ACL , 2007
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D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473 , 2014
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2014
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I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in neural information processing systems , 2014, pp. 3104–3112
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D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
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A. M. Rush, S. Chopra, and J. Weston, “A neural attention model for abstractive sentence summarization,” arXiv preprint arXiv:1509.00685 , 2015
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2015
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M.-T. Luong, H. Pham, and C. D. Manning, “Effective approaches to attention-based neural machine translation,” arXiv preprint arXiv:1508.04025 , 2015
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C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1–9
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R. Sennrich, B. Haddow, and A. Birch, “Neural machine translation of rare words with subword units,” arXiv preprint arXiv:1508.07909 , 2015
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2015
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M.-T. Luong and C. D. Manning, “Stanford neural machine translation systems for spoken language domains,” in Proceedings of the International Workshop on Spoken Language Translation , 2015
2015
Earlier work this paper cites.

Similar

X. Shi, K. Knight, and D. Yuret, “Why neural translations are the right length,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , 2016, pp. 2278–2282
2016
Cited alongside, same era.
Y. Kikuchi, G. Neubig, R. Sasano, H. Takamura, and M. Okumura, “Controlling output length in neural encoder-decoders,” arXiv preprint arXiv:1609.09552 , 2016
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Cited alongside, same era.
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2016
Cited alongside, same era.

Then

S. Läubli, R. Sennrich, and M. Volk, “Has machine translation achieved human parity? A case for document-level evaluation,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , Brussels, Belgium, 2018, pp. 4791–4796. [Online]. Available: https://aclanthology.info/papers/D18-1512/d18-1512
2018
Later among the works it cites.
K. Murray and D. Chiang, “Correcting length bias in neural machine translation,” in Proceedings of the Third Conference on Machine Translation: Research Papers , 2018, pp. 212–223
2018
Later among the works it cites.
Y. Liu, Z. Luo, and K. Zhu, “Controlling length in abstractive summarization using a convolutional neural network,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , 2018, pp. 4110–4119
2018
Later among the works it cites.
J. Kreutzer and A. Sokolov, “Learning to segment inputs for nmt favors character-level processing,” 2018
2018
Later among the works it cites.

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2019

Controlling the Output Length of Neural Machine Translation

Lakew, Surafel Melaku, Di Gangi, Mattia, Federico, Marcello

Understand

The recent advances introduced by neural machine translation (NMT) are rapidly expanding the application fields of machine translation, as well as reshaping the quality level to be targeted.

In particular, if translations have to fit some given layout, quality should not only be measured in terms of adequacy and fluency, but also length.
Exemplary cases are the translation of document files, subtitles, and scripts for dubbing, where the output length should ideally be as close as possible to the length of the input text.
This paper addresses for the first time, to the best of our knowledge, the problem of controlling the output length in NMT.

Built on

T. Sho and O. Naoaki, “Positional encoding to control output sequence length,” in Proceedings of the HLT-NAACL 2019 , 2019. [Online]. Available: http://arxiv.org/abs/1904.07418
Original
1904
Earlier work this paper cites.
E. Noreen, Computer-Intensive Methods for Testing Hypotheses: An Introduction . Wiley, 1989
1989
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting on association for computational linguistics . Association for Computational Linguistics, 2002, pp. 311–318
2002
Earlier work this paper cites.
P. Koehn, H. Hoang, A. Birch, C. Callison-Burch et al. , “Moses: Open source toolkit for statistical machine translation,” in Proc. of ACL , 2007
2007
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473 , 2014
Original
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in neural information processing systems , 2014, pp. 3104–3112
2014
Earlier work this paper cites.
D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
A. M. Rush, S. Chopra, and J. Weston, “A neural attention model for abstractive sentence summarization,” arXiv preprint arXiv:1509.00685 , 2015
Original
2015
Earlier work this paper cites.
M.-T. Luong, H. Pham, and C. D. Manning, “Effective approaches to attention-based neural machine translation,” arXiv preprint arXiv:1508.04025 , 2015
Original
2015
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1–9
2015
Earlier work this paper cites.
R. Sennrich, B. Haddow, and A. Birch, “Neural machine translation of rare words with subword units,” arXiv preprint arXiv:1508.07909 , 2015
Original
2015
Earlier work this paper cites.
M.-T. Luong and C. D. Manning, “Stanford neural machine translation systems for spoken language domains,” in Proceedings of the International Workshop on Spoken Language Translation , 2015
2015
Earlier work this paper cites.

Similar

X. Shi, K. Knight, and D. Yuret, “Why neural translations are the right length,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , 2016, pp. 2278–2282
2016
Cited alongside, same era.
Y. Kikuchi, G. Neubig, R. Sasano, H. Takamura, and M. Okumura, “Controlling output length in neural encoder-decoders,” arXiv preprint arXiv:1609.09552 , 2016
Original
2016
Cited alongside, same era.
M. Johnson, M. Schuster, Q. V. Le, M. Krikun, Y. Wu, Z. Chen, N. Thorat, F. Viégas, M. Wattenberg, G. Corrado et al. , “Google’s multilingual neural machine translation system: Enabling zero-shot translation,” arXiv preprint arXiv:1611.04558 , 2016
Original
2016
Cited alongside, same era.
T.-L. Ha, J. Niehues, and A. Waibel, “Toward multilingual neural machine translation with universal encoder and decoder,” arXiv preprint arXiv:1611.04798 , 2016
Original
2016
Cited alongside, same era.

Then

S. Läubli, R. Sennrich, and M. Volk, “Has machine translation achieved human parity? A case for document-level evaluation,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , Brussels, Belgium, 2018, pp. 4791–4796. [Online]. Available: https://aclanthology.info/papers/D18-1512/d18-1512
2018
Later among the works it cites.
K. Murray and D. Chiang, “Correcting length bias in neural machine translation,” in Proceedings of the Third Conference on Machine Translation: Research Papers , 2018, pp. 212–223
2018
Later among the works it cites.
Y. Liu, Z. Luo, and K. Zhu, “Controlling length in abstractive summarization using a convolutional neural network,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , 2018, pp. 4110–4119
2018
Later among the works it cites.
J. Kreutzer and A. Sokolov, “Learning to segment inputs for nmt favors character-level processing,” 2018
2018
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

B. Zoph, D. Yuret, J. May, and K. Knight, “Transfer learning for low-resource neural machine translation,” arXiv preprint arXiv:1604.02201 , 2016

Original

2016

Cited alongside, same era.

Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey et al. , “Google’s neural machine translation system: Bridging the gap between human and machine translation,” arXiv preprint arXiv:1609.08144 , 2016

Original

2016

Cited alongside, same era.

R. Sennrich, B. Haddow, and A. Birch, “Controlling politeness in neural machine translation via side constraints,” in Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2016, pp. 35–40

2016

Cited alongside, same era.

A. Fan, D. Grangier, and M. Auli, “Controllable abstractive summarization,” arXiv preprint arXiv:1711.05217 , 2017

Original

2017

Cited alongside, same era.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , 2017, pp. 6000–6010

2017

Cited alongside, same era.

M. A. Farajian, M. Turchi, M. Negri, and M. Federico, “Multi-domain neural machine translation through unsupervised adaptation,” in Proceedings of the Second Conference on Machine Translation , 2017, pp. 127–137

2017

Cited alongside, same era.

L. Bentivogli, A. Bisazza, M. Cettolo, and M. Federico, “Neural versus phrase-based mt quality: An in-depth analysis on english-german and english-french,” Computer Speech & Language , vol. 49, pp. 52–70, 2018

2018

Cited alongside, same era.

H. Hassan, A. Aue, C. Chen, V. Chowdhary, J. Clark, C. Federmann, X. Huang, M. Junczys-Dowmunt, W. Lewis, M. Li, S. Liu, T. Liu, R. Luo, A. Menezes, T. Qin, F. Seide, X. Tan, F. Tian, L. Wu, S. Wu, Y. Xia, D. Zhang, Z. Zhang, and M. Zhou, “Achieving human parity on automatic chinese to english news translation,” CoRR , vol. abs/1803.05567, 2018. [Online]. Available: http://arxiv.org/abs/1803.05567

Original

2018

Cited alongside, same era.

C. Cherry, G. Foster, A. Bapna, O. Firat, and W. Macherey, “Revisiting character-based neural machine translation with capacity and compression,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , 2018, pp. 4295–4305

2018