Michael Coury


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The MITLL-AFRL IWSLT 2014 MT system
Michaeel Kazi | Elizabeth Salesky | Brian Thompson | Jessica Ray | Michael Coury | Tim Anderson | Grant Erdmann | Jeremy Gwinnup | Katherine Young | Brian Ore | Michael Hutt
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

This report summarizes the MITLL-AFRL MT and ASR systems and the experiments run using them during the 2014 IWSLT evaluation campaign. Our MT system is much improved over last year, owing to integration of techniques such as PRO and DREM optimization, factored language models, neural network joint model rescoring, multiple phrase tables, and development set creation. We focused our eforts this year on the tasks of translating from Arabic, Russian, Chinese, and Farsi into English, as well as translating from English to French. ASR performance also improved, partly due to increased eforts with deep neural networks for hybrid and tandem systems. Work focused on both the English and Italian ASR tasks.


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The MIT-LL/AFRL IWSLT-2013 MT system
Michaeel Kazi | Michael Coury | Elizabeth Salesky | Jessica Ray | Wade Shen | Terry Gleason | Tim Anderson | Grant Erdmann | Lane Schwartz | Brian Ore | Raymond Slyh | Jeremy Gwinnup | Katherine Young | Michael Hutt
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the MIT-LL/AFRL statistical MT system and the improvements that were developed during the IWSLT 2013 evaluation campaign [1]. As part of these efforts, we experimented with a number of extensions to the standard phrase-based model that improve performance on the Russian to English, Chinese to English, Arabic to English, and English to French TED-talk translation task. We also applied our existing ASR system to the TED-talk lecture ASR task. We discuss the architecture of the MIT-LL/AFRL MT system, improvements over our 2012 system, and experiments we ran during the IWSLT-2013 evaluation. Specifically, we focus on 1) cross-entropy filtering of MT training data, and 2) improved optimization techniques, 3) language modeling, and 4) approximation of out-of-vocabulary words.