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Arabian Journal for Science and Engineering

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08-02-2022 | Research Article-Computer Engineering and Computer Science

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

Authors: Amel Slim, Ahlem Melouah, Usef Faghihi, Khouloud Sahib

Published in: Arabian Journal for Science and Engineering | Issue 8/2022

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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.

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Title: Improving Neural Machine Translation for Low Resource Algerian Dialect by Transductive Transfer Learning Strategy
Authors: Amel Slim
Ahlem Melouah
Usef Faghihi
Khouloud Sahib
Publication date: 08-02-2022
Publisher: Springer Berlin Heidelberg
Published in: Arabian Journal for Science and Engineering / Issue 8/2022
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-022-06588-w

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