Publications

Refine Results

(Filters Applied) Clear All

The AFRL-MITLL WMT16 news-translation task systems

Published in:
Proc. First Conf. on Machine Translation, Vol. 2, 11-12 August 2016, pp. 296-302.

Summary

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 generation.
READ LESS

Summary

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 generation.

READ MORE

The MITLL-AFRL IWSLT 2015 Systems

Summary

This report summarizes the MITLL-AFRL MT, 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 Chinese. ASR systems are also improved by reining improvements developed last year. Finally, we combine our ASR and MT systems to produce a English to Chinese SLT system.
READ LESS

Summary

This report summarizes the MITLL-AFRL MT, 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...

READ MORE

The AFRL-MITLL WMT15 System: there's more than one way to decode it!

Published in:
Proc. 10th Workshop on Statistical Machine Translation, 17-18 September 2015, pp. 112-9.

Summary

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 phrase-based model that improve performance on the Russian to English translation task creating three submission systems with different decoding strategies. Out of vocabulary words were addressed with named entity postprocessing.
READ LESS

Summary

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 phrase-based model that improve performance on the Russian to English translation task creating three submission systems...

READ MORE

The MITLL/AFRL IWSLT-2014 MT System

Summary

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 efforts 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 efforts with deep neural networks for hybrid and tandem systems. Work focused on both the English and Italian ASR tasks.
READ LESS

Summary

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...

READ MORE

The MIT-LL/AFRL IWSLT-2013 MT System

Summary

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.
READ LESS

Summary

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...

READ MORE

Showing Results

1-5 of 5