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Robust Neural Machine Translation for Clean and Noisy Speech Transcripts

Di Gangi, Mattia Antonino; Enyedi, Robert; Brusadin, Alessandra; Federico, Marcello;

Robust Neural Machine Translation for Clean and Noisy Speech Transcripts

Abstract

Neural machine translation models have shown to achieve high quality when trained and fed with well structured and punctuated input texts. Unfortunately, the latter condition is not met in spoken language translation, where the input is generated by an automatic speech recognition (ASR) system. In this paper, we study how to adapt a strong NMT system to make it robust to typical ASR errors. As in our application scenarios transcripts might be post-edited by human experts, we propose adaptation strategies to train a single system that can translate either clean or noisy input with no supervision on the input type. Our experimental results on a public speech translation data set show that adapting a model on a significant amount of parallel data including ASR transcripts is beneficial with test data of the same type, but produces a small degradation when translating clean text. Adapting on both clean and noisy variants of the same data leads to the best results on both input types.

6 pages, accepted at IWSLT 2019

Keywords

Computer Science - Computation and Language, Computer Science - Machine Learning, Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences

47 references, page 1 of 5

[1] O. Bojar, R. Chatterjee, C. Federmann, Y. Graham, B. Haddow, S. Huang, M. Huck, P. Koehn, Q. Liu, V. Logacheva et al., “Findings of the 2017 conference on machine translation (wmt17),” in Proceedings of the Second Conference on Machine Translation, 2017, pp. 169-214.

[2] O. Bojar, C. Federmann, M. Fishel, Y. Graham, B. Haddow, M. Huck, P. Koehn, and C. Monz, “Findings of the 2018 Conference on Machine Translation (WMT18),” in Proceedings of WMT 2018, 2018, pp. 272-307.

[3] I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to Sequence Learning with Neural Networks,” in Proceedings of NIPS 2014, 2014.

[4] D. Bahdanau, K. Cho, and Y. Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate,” in Proceedings of ICLR 2015, 2015.

[5] M. Junczys-Dowmunt, “Microsoft's submission to the wmt2018 news translation task: How i learned to stop worrying and love the data,” in Proceedings of the Third Conference on Machine Translation: Shared Task Papers, 2018, pp. 425-430. [OpenAIRE]

[6] P. Koehn, H. Khayrallah, K. Heafield, and M. L. Forcada, “Findings of the wmt 2018 shared task on parallel corpus filtering,” in Proceedings of the Third Conference on Machine Translation: Shared Task Papers, 2018, pp. 726-739. [OpenAIRE]

[7] Y. Belinkov and Y. Bisk, “Synthetic and natural noise both break neural machine translation,” Proceedings of ICLR, 2018.

[8] N. Ruiz, M. A. Di Gangi, N. Bertoldi, and M. Federico, “Assessing the tolerance of neural machine translation systems against speech recognition errors,” Proc. Interspeech 2017, pp. 2635-2639, 2017.

[9] V. Karpukhin, O. Levy, J. Eisenstein, and M. Ghazvininejad, “Training on synthetic noise improves robustness to natural noise in machine translation,” arXiv preprint arXiv:1902.01509, 2019.

[10] M. Sperber, J. Niehues, and A. Waibel, “Toward robust neural machine translation for noisy input sequences,” in International Workshop on Spoken Language Translation (IWSLT)., 2017. [OpenAIRE]

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impulse
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