Neural machine translation is currently a hot topic in the industry, mainly due to some claims that the output can reach human translation quality level. Unlike statistical machine translation where translations are produced based on statistical models, neural machine translation is using neural networks and machine learning technology to transfer the meaning of the source text to the target language.Research has shown that NeuralMT produces better output in terms of fluency, while SMT gives better results in terms of adequacy. However,NMT post-editing experiments have shown that the fluent output does not necessarily mean a correct translation.

Proposed sub-topics for discussion:

  • The impact of NMT on the translation industry in general
  • The impact of NMT on the post-editing process:
    • Since NMT behaves like a "black box", how should post-editors correct the NMT output? What strategies should they learn?
    • Issues in quality evaluation

Last updated: 05 Jan 2018 01:02, by: Iulianna van der Lek