Low-resource Neural Machine Translation from Finnish to Chinese

Neural machine translation (NMT) has been a mainstream method for the machine translation (MT) task. Despite its remarkable progress, NMT systems still face many challenges when dealing with low-resource scenarios. Common approaches to address the data scarcity problem include exploiting monolingual data or parallel data in other languages. In this thesis, transformer-based NMT models are trained on Finnish-Simplified Chinese, a language pair with limited parallel data and the models are improved using various techniques such as hyperparameter tuning, transfer learning and back-translation. Finally, the best NMT system is an ensemble model that combines different single models. The results of our experiments also show that different hyperparameter settings can cause a performance gap of up to 4 BLEU scores. The ensemble model shows a 35% improvement over the baseline model. Overall, the experiments suggest that hyperparameter tuning is crucial for training vanilla NMT models. Back-translation offers more benefits for model improvement than the transfer learning method. The results also show that adding sampling in back-translation does not improve NMT model performance in this low-data setting. The findings may be useful for future research on low-resource NMT, especially the Finnish-Simplified Chinese MT task