Publication: Domain robustness in neural machine translation
Domain robustness in neural machine translation
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Müller, M., Rios, A., & Sennrich, R. (2020). Domain robustness in neural machine translation. Proceedings of the 14th Conference of the Association for Machine Translation in the Americas, 151–164. https://www.aclweb.org/anthology/2020.amta-research.14
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Translating text that diverges from the training domain is a key challenge for machine translation. Domain robustnes - the generalization of models to unseen test domains - is low for both statistical (SMT) and neural machine translation (NMT). In this paper, we study the performance of SMT and NMT models on out-of-domain test sets. We find that in unknown domains, SMT and NMT suffer from very different problems: SMT systems are mostly adequate but not fluent, while NMT systems are mostly fluent, but not adequate. For NMT, we identi
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Müller, M., Rios, A., & Sennrich, R. (2020). Domain robustness in neural machine translation. Proceedings of the 14th Conference of the Association for Machine Translation in the Americas, 151–164. https://www.aclweb.org/anthology/2020.amta-research.14
