Assessing the Efficacy of Synthetic Data for Enhancing Machine Translation Models in Low Resource Domains

Author:

Yadav Shweta

Publisher

Springer Nature Switzerland

Reference17 articles.

1. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

2. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate (2014). arXiv preprint arXiv:1409.0473

3. Koehn, P., Knowles, R.: Six challenges for neural machine translation (2017). arXiv preprint arXiv:1706.03872

4. Kumar, S., Anastasopoulos, A., Wintner, S., Tsvetkov, Y.: Machine translation into low-resource language varieties (2021). arXiv preprint arXiv:2106.06797

5. Luong, M.T., Manning, C.D.: Stanford neural machine translation systems for spoken language domains. In: Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign, pp. 76–79 (2015)

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