Ternary Data, Triangle Decoding, Three Tasks, a Multitask Learning Speech Translation Model-Reference-Cited by-同舟云学术

Ternary Data, Triangle Decoding, Three Tasks, a Multitask Learning Speech Translation Model

Published:2023 Issue: Volume: Page:579-590
ISSN:0302-9743
Container-title:Artificial Neural Networks and Machine Learning – ICANN 2023
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Author:

Zhang Linlin,Chen Boxing,Zhu Shaolin,Si Luo

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-44213-1_48

Reference28 articles.

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2. Anastasopoulos, A., Chiang, D.: Tied multitask learning for neural speech translation. In: Walker, M.A., Ji, H., Stent, A. (eds.) Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, 1–6 June 2018, vol. 1 (Long Papers), pp. 82–91. Association for Computational Linguistics (2018). https://doi.org/10.18653/v1/n18-1008

3. Bahar, P., et al.: Start-before-end and end-to-end: neural speech translation by apptek and RWTH Aachen university. In: Federico, M., Waibel, A., Knight, K., Nakamura, S., Ney, H., Niehues, J., Stüker, S., Wu, D., Mariani, J., Yvon, F. (eds.) Proceedings of the 17th International Conference on Spoken Language Translation, IWSLT 2020, Online, 9–10 July 2020, pp. 44–54. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.iwslt-1.3

4. Bapna, A., Firat, O.: Simple, scalable adaptation for neural machine translation. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, 3–7 November 2019, pp. 1538–1548. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/D19-1165

5. Berard, A., Pietquin, O., Servan, C., Besacier, L.: Listen and translate: a proof of concept for end-to-end speech-to-text translation. CoRR abs/1612.01744 (2016). http://arxiv.org/abs/1612.01744