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Neural Machine Translation for the Arabic-English Language Pair

March 2024 Aliane Amine ICPAIS (International Conference on Pattern Analysis and Intelligent Systems)
#NLP #Machine Translation #Neural Networks #Arabic

Abstract

This work implements and evaluates modern neural machine translation architectures on the Arabic-English language pair, a setting where morphological complexity and limited high-quality parallel data pose challenges beyond those typically faced by well-resourced language pairs.

Method

Transformer-based sequence-to-sequence models are trained and evaluated on Arabic-English parallel corpora, with results compared across architectural and training variants.

Contribution

Provides an evaluation of neural machine translation performance for Arabic-English, establishing a baseline that later informed the model efficiency and pruning work developed for multilingual NMT.

This paper’s translation setup is the foundation for A General Framework for Efficient Multilingual Neural Machine Translation, which builds on it with model compression techniques.