Transformer Meets External Context: A Novel Approach to Enhance Neural Machine Translation

Most neural machine translation (NMT) systems rely on parallel data, comprising text in the source language and its corresponding translation in the target language. While it’s acknowledged that context enhances NMT models, this work proposes a novel approach by incorporating external context, specifically explanations of source text meanings, akin to how human translators leverage context for comprehension. The suggested methodology innovatively addresses the challenge of incorporating lengthy contextual information into NMT systems. By employing state-of-the-art transformer-based models, external context is integrated, thereby enriching the translation process. A key aspect of the approach lies in the utilization of diverse text summarization techniques, strategically employed to efficiently distill extensive contextual details into the NMT framework. This novel solution not only overcomes the obstacle posed by lengthy context but also enhances the translation quality, marking a advancement in the field of NMT. Furthermore, the data-centric approach ensures robustness and effectiveness, yielding improvements in translation quality, as evidenced by a considerable boost in BLEU score points ranging from 0.46 to 1.87 over baseline models. Additionally, we make our dataset publicly available, facilitating further research in this domain.

@article{Alsuhaibani2024,
  title     = {Transformer Meets External Context: A Novel Approach to Enhance Neural Machine Translation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {5},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Mohammed Alsuhaibani and Kamel Gaanoun and Ali Alsohaibani},
  doi       = {10.14569/IJACSA.2024.01505135},
  url       = {https://doi.org/10.14569/IJACSA.2024.01505135}
}