Multi-Source Neural Machine Translation With Missing Data

IEEE/ACM Transactions on Audio, Speech, and Language Processing, pp. 569-580, 2020.

Cited by: 0|Bibtex|Views12|DOI:https://doi.org/10.1109/TASLP.2019.2959224
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Abstract:

Machine translation is rife with ambiguities in word ordering and word choice, and even with the advent of machine-learning methods that learn to resolve this ambiguity based on statistics from large corpora, mistakes are frequent. Multi-source translation is an approach that attempts to resolve these ambiguities by exploiting multiple in...More

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