Improving Neural Machine Translation for Low Resource Algerian Dialect by Transductive Transfer Learning Strategy

Abstract

This study is the first work on a transductive transfer learning approach for low-resource neural machine translation applied to the Algerian Arabic dialect. The transductive approach is based on a fine-tuning transfer learning strategy that transfers knowledge from the parent model to the child model. This strategy helps to solve the learning problem using limited parallel corpora. We tested the approach on a sequence-to-sequence model with and without the Attention mechanism. We first trained the models on a parallel multi-dialects Arabic corpus and then switch them to a low-resource of the Algerian dialect. Transductive transfer learning raises the BLEU score for the Seq2Seq model from 0.3 to more than 34, and for the Attentional-Seq2Seq model from less than 17 to more than 35. The obtained results prove the validity of this approach.