Search Results for author: Te I

Found 2 papers, 1 papers with code

Sentence Boundary Augmentation For Neural Machine Translation Robustness

no code implementations21 Oct 2020 Daniel Li, Te I, Naveen Arivazhagan, Colin Cherry, Dirk Padfield

Specifically, in the context of long-form speech translation systems, where the input transcripts come from Automatic Speech Recognition (ASR), the NMT models have to handle errors including phoneme substitutions, grammatical structure, and sentence boundaries, all of which pose challenges to NMT robustness.

Automatic Speech Recognition Data Augmentation +2

Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translation

1 code implementation6 Dec 2019 Naveen Arivazhagan, Colin Cherry, Te I, Wolfgang Macherey, Pallavi Baljekar, George Foster

As this scenario allows for revisions to our incremental translations, we adopt a re-translation approach to simultaneous translation, where the source is repeatedly translated from scratch as it grows.

Machine Translation Speech Recognition +1

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