Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain
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Abstract (arXiv)
Multilingual Neural Machine Translation (MNMT) models leverage many language pairs during training to improve translation quality for low-resource languages by transferring knowledge from high-resource languages. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation which includes terminology-specific error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer terminology errors.
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References · 11
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- 2016Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translationcited by 467
- 2018A Survey of Domain Adaptation for Neural Machine Translationcited by 64
- 2020Optimizing Transformer for Low-Resource Neural Machine Translationcited by 24
- 2021Continual Learning in Multilingual NMT via Language-Specific Embeddingscited by 12
- 2020A Multilingual Neural Machine Translation Model for Biomedical Datacited by 7
- 2022How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?cited by 3
- 2017Fine-grained human evaluation of neural versus phrase-based machine translationcited by 2
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