Boğaziçi University researchers reveal the potential of customizing MT systems to replicate a translator's style, leading to literary translations that mirror the translator's unique style.
Unlike some fields in machine learning, machine translation still requires large sets of training data. The solution? Creating more data when none (or not enough) exists.
New paper by Facebook, Amazon, Twitter, and University of Melbourne examines vulnerability of systems using back translation to attacks on monolingual training data.
500-million-sentence dataset from Jörg Tiedemann, Professor at University of Helsinki, can improve back translation by centralizing monolingual content for 188 languages.
A group of AI researchers takes the perennial human vs. machine debate to the next level by claiming their system outperforms professional translators...in news, on adequacy (read on for more caveats).
Google has admitted machine translated content is a challenge to ranking algorithms. Researchers at the University of Tokyo may have found a solution: back-translation.
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