@article{Yune2024,
  author = {Florance Yune and Khin Mar Soe},
  title = {Tracing the Evolution of Machine Translation: A Journey through the Myanmar (Burmese)-Wa (sub-group of the Austro-Asiatic language) Corpus},
  journal = {Advances in Science, Technology and Engineering Systems Journal},
  year = {2024},
  volume = {9},
  number = {1},
  pages = {79–90},
  doi = {10.25046/aj090108},
  url = {https://www.astesj.com/v09/i01/p08/},
  language = {en},
  publisher = {ASTES Publishers},
  abstract = {<p style="text-align: justify;">Machine Translation (MT) has come a long way toward reducing linguistic gaps. However, its progress in efficiently handling low-resource languages—such as the Wa language in the Myanmar-Wa corpus—has not received enough attention. This study begins with a thorough investigation of the historical development of MT systems, painstakingly following their development against the complex background of the Myanmar-Wa language region. Using an interdisciplinary methodology that integrates linguistics, technology, and culture, this investigation reveals the transformative journey of Machine Translation (MT) in its pursuit of overcoming linguistic barriers. It offers a thorough study that clarifies the opportunities and limitations present in MT’s progress. More broadly, by clarifying the complex relationship between technology and linguistic diversity, our work not only advances our understanding of MT’s evolutionary history but also supports the conservation of endangered languages, like Wa language. The research’s conclusions have implications that go beyond machine translation to the larger conversation about language preservation and how technology development coexists harmoniously. Notably, this paper is an extension of work originally presented in “2023 IEEE Conference on Computer Applications (ICCA)”, acknowledging its foundation and presenting substantial advancements.</p>},
  keywords = {Machine Translation (MT), Low-resource Languages Myanmar-Wa Corpus, Language Preservation, Transfer Learning, Long Short-Term Memory, Recurrent Neural, Network, Pivot-Based SMT}
}