Michaeel Kazi

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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(More)
This paper describes the AFRL-MITLL statistical MT systems and the improvements that were developed during the WMT15 evaluation campaign. As part of these efforts we experimented with a number of extensions to the standard phrasebased model that improve performance on the Russian to English translation task creating three submission systems with different(More)
This paper describes the AFRL-MITLL statistical machine translation systems and the improvements that were developed during the WMT16 evaluation campaign. New techniques applied this year include Neural Machine Translation, a unique selection process for language modelling data, additional out-of-vocabulary transliteration techniques, and morphology(More)
This report summarizes the MITLL-AFRLMT, ASR and SLT systems and the experiments run using them during the 2015 IWSLT evaluation campaign. We build on the progress made last year, and additionally experimented with neural MT, unknown word processing, and system combination. We applied these techniques to translating Chinese to English and English to(More)
This paper describes the AFRL-MITLL machine translation systems and the improvements that were developed during the WMT17 evaluation campaign. This year, we explore the continuing proliferation of Neural Machine Translation toolkits, revisit our previous data-selection efforts for use in training systems with these new toolkits and expand our participation(More)
This paper describes the AFRL-MITLL statistical machine translation systems and the improvements that were developed during the WMT16 evaluation campaign. As part of these efforts we’ve adapted a variety new techniques to our previous years’ systems including Neural Machine Translation, additional out-of-vocabulary transliteration techniques, and morphology(More)
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