Future (Present?) of Machine Translation

  • Kyunghyun Cho | New York University

It is quite easy to believe that the recently proposed approach to machine translation, called neural machine translation, is simply yet another approach to statistical machine translation. This belief may drive research effort toward (incrementally) improving the existing neural machine translation system to outperform, or perform comparably to, the existing variants of phrase-based systems. In this talk, I aim to convince you otherwise. I argue that neural machine translation is not here to compete against the existing translation systems, but to open new opportunities in the field of machine translation. I will discuss three opportunities; (1) sub-word-level translation, (2) larger-context translation and (3) multilingual translation.

Series: Microsoft Research Talks