Sánchez-Cartagena & al. (2020)

De Arbres
  • Sánchez-Cartagena, Víctor M., Mikel L. Forcada, & Felipe Sánchez-Martínez. 2020. 'A multi-source approach for Breton–French hybrid machine translation', Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, Lisboa, Portugal. European Association for Machine Translation, 61–70. texte.

étude en traitement automatique des langues de la qualité d'un système hybride de traduction automatique entre le breton et le français, qui utilise à la fois un système neuronal et un système basé sur des règles. En dépit du fait que le système basé sur des règles a des performances jugées pauvres, sa combinaison avec le système neuronal améliore la qualité globale des résultats obtenus.

 "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. This paper focuses on the hybridisation of rule-based MT and neural MT for the Breton–French under-resourced language pair in an attempt to study to what extent the rule-based MT resources help improve the translation quality of the neural MT system for this particular under-resourced language pair. We combine both translation approaches in a multi-source neural MT architecture and find out that, even though the rule-based system has a low performance according to automatic evaluation metrics, using it leads to improved translation quality."