Katherine Young


2018

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The AFRL WMT18 Systems: Ensembling, Continuation and Combination
Jeremy Gwinnup | Tim Anderson | Grant Erdmann | Katherine Young
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the Air Force Research Laboratory (AFRL) machine translation systems and the improvements that were developed during the WMT18 evaluation campaign. This year, we examined the developments and additions to popular neural machine translation toolkits and measure improvements in performance on the Russian–English language pair.

2017

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The AFRL-MITLL WMT17 Systems: Old, New, Borrowed, BLEU
Jeremy Gwinnup | Timothy Anderson | Grant Erdmann | Katherine Young | Michaeel Kazi | Elizabeth Salesky | Brian Thompson | Jonathan Taylor
Proceedings of the Second Conference on Machine Translation

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The AFRL WMT17 Neural Machine Translation Training Task Submission
Grant Erdmann | Katherine Young | Jeremy Gwinnup
Proceedings of the Second Conference on Machine Translation

2016

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The AFRL-MITLL WMT16 News-Translation Task Systems
Jeremy Gwinnup | Tim Anderson | Grant Erdmann | Katherine Young | Michaeel Kazi | Elizabeth Salesky | Brian Thompson
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

2015

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The MITLL-AFRL IWSLT 2015 MT system
Michaeel Kazi | Brian Thompson | Elizabeth Salesky | Timothy Anderson | Grant Erdmann | Eric Hansen | Brian Ore | Katherine Young | Jeremy Gwinnup | Michael Hutt | Christina May
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

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The AFRL-MITLL WMT15 System: There’s More than One Way to Decode It!
Jeremy Gwinnup | Tim Anderson | Grant Erdmann | Katherine Young | Christina May | Michaeel Kazi | Elizabeth Salesky | Brian Thompson
Proceedings of the Tenth Workshop on Statistical Machine Translation

2014

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The MITLL-AFRL IWSLT 2014 MT system
Michael 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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Machine Translation and Monolingual Postediting: The AFRL WMT-14 System
Lane Schwartz | Timothy Anderson | Jeremy Gwinnup | Katherine Young
Proceedings of the Ninth Workshop on Statistical Machine Translation

2013

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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.

2012

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Reversing the Palladius Mapping of Chinese Names in Russian Text
Katherine Young
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Government MT User Program

We present the Reverse Palladius (RevP) program developed by the Air Force Research Laboratory's Speech and Communication Research, Engineering, Analysis, and Modeling (SCREAM) Laboratory for the National Air and Space Intelligence Center (NASIC). The RevP program assists the linguist in correcting the transliteration of Mandarin Chinese names during the Russian to English translation process. Chinese names cause problems for transliteration, because Russian writers follow a specific Palladius mapping for Chinese sounds. Typical machine translation of Russian into English then applies standard transliteration of the Russian sounds in these names, producing errors that require hand-correction. For example, the Chinese name Zhai Zhigang is written in Cyrillic as Чжай Чжиган, and standard transliteration via Systran renders this into English as Chzhay Chzhigan. In contrast, the RevP program uses rules that reverse the Palladius mapping, yielding the correct form Zhai Zhigang. When using the RevP program, the linguist opens a Russian document and selects a Chinese name for transliteration. The rule-based algorithm proposes a reverse Palladius transliteration, as well as a stemmed option if the word terminates in a possible Russian inflection. The linguist confirms the appropriate version of the name, and the program both corrects the current instance and stores the information for future use. The resulting list of name mappings can be used to pre-translate names in new documents, either via stand-alone operation of the RevP program, or through compilation of the list as a Systran user dictionary. The RevP program saves time by removing the need for post-editing of Chinese names, and improves consistency in the translation of these names. The user dictionary becomes more useful over time, further reducing the time required for translation of new documents.