Search Results for author: Jacob Bremerman

Found 5 papers, 1 papers with code

The JHU Submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education

no code implementations WS 2020 Huda Khayrallah, Jacob Bremerman, Arya D. McCarthy, Kenton Murray, Winston Wu, Matt Post

This paper presents the Johns Hopkins University submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education (STAPLE).

Machine Translation Translation

The Multilingual TEDx Corpus for Speech Recognition and Translation

no code implementations2 Feb 2021 Elizabeth Salesky, Matthew Wiesner, Jacob Bremerman, Roldano Cattoni, Matteo Negri, Marco Turchi, Douglas W. Oard, Matt Post

We present the Multilingual TEDx corpus, built to support speech recognition (ASR) and speech translation (ST) research across many non-English source languages.

speech-recognition Speech Recognition +1

Machine Translation Robustness to Natural Asemantic Variation

1 code implementation25 May 2022 Jacob Bremerman, Xiang Ren, Jonathan May

We find that existing MT models fail when presented with NAV data, but we demonstrate strategies to improve performance on NAV by fine-tuning them with human-generated variations.

Machine Translation Translation

FINDINGS OF THE IWSLT 2021 EVALUATION CAMPAIGN

no code implementations ACL (IWSLT) 2021 Antonios Anastasopoulos, Ondřej Bojar, Jacob Bremerman, Roldano Cattoni, Maha Elbayad, Marcello Federico, Xutai Ma, Satoshi Nakamura, Matteo Negri, Jan Niehues, Juan Pino, Elizabeth Salesky, Sebastian Stüker, Katsuhito Sudoh, Marco Turchi, Alexander Waibel, Changhan Wang, Matthew Wiesner

The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2021) featured this year four shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Multilingual speech translation, (iv) Low-resource speech translation.

Translation

On the Evaluation of Machine Translation n-best Lists

no code implementations EMNLP (Eval4NLP) 2020 Jacob Bremerman, Huda Khayrallah, Douglas Oard, Matt Post

The first and principal contribution is an evaluation measure that characterizes the translation quality of an entire n-best list by asking whether many of the valid translations are placed near the top of the list.

Machine Translation Translation +1

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