Exploring Benefits of Transfer Learning in Neural Machine Translation

Title: Exploring Benefits of Transfer Learning in Neural Machine Translation Author: Tom Kocmi Department: Institute of Formal and Applied Linguistics Supervisor: doc. RNDr. Ondřej Bojar, Ph.D., Institute of Formal and Applied Linguistics Keywords: transfer learning, machine translation, deep neural networks, low-resource languages Abstract: Neural machine translation is known to require large numbers of parallel train- ing sentences, which generally prevent it from excelling on low-resource lan- guage pairs. This thesis explores the use of cross-lingual transfer learning on neural networks as a way of solving the problem with the lack of resources. We propose several transfer learning approaches to reuse a model pretrained on a high-resource language pair. We pay particular attention to the simplicity of the techniques. We study two scenarios: (a) when we reuse the high-resource model without any prior modifications to its training process and (b) when we can prepare the first-stage high-resource model for transfer learning in advance. For the former scenario, we present a proof-of-concept method by reusing a model trained by other researchers. In the latter scenario, we present a method which reaches even larger improvements in translation performance. Apart from proposed techniques, we focus on an..