From Translation Divergences to Structure-aware Neural Machine Translation
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If you have a question about this talk, please contact Marinela Parovic.
Languages present a wide range of structures for expressing similar
meanings. This variation has historically motivated the integration of
linguistic structure into machine translation (MT) models, so as to
abstract away from realization differences, but such integration has
been receiving less attention since the introduction of neural MT
models. In my talk I will discuss ongoing work we’re carrying out in
the lab, on characterizing divergences and their impact on the
performance in today’s neural MT models, as well as on two approaches
for integrating syntactic structure into MT models to address this
gap.
Joint work with many lab members and collaborators, notably Leshem
Choshen, Dmitry Nikolaev, Asaf Yehudai and Lior Fox.
This talk is part of the Language Technology Lab Seminars series.
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