Corpus-based approaches to machine translation (MT) have difficulties when the amount of parallel corpora to use for training is scarce, especially if the languages involved in the translation are highly inflected. This problem can be addressed from different perspectives, including data augmentation, transfer learning, and the use of additional resources, such as those used in rule-based MT (RBMT). This paper focuses on the hybridisation of RBMT and neural MT (NMT) for the Breton-French under-resourced language pair in an attempt to study to what extent the RBMT resources help improve the translation quality of the NMT system. We combine both translation approaches in a multi-source NMT architecture and find out that, even though the RBMT system has a low performance according to automatic evaluation metrics, using it leads to improved translation quality.
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