This report summarizes the Air Force Research Laboratory (AFRL) machine translation (MT) and automatic speech recognition (ASR) systems submitted to the spoken language translation (SLT) and low-resource MT tasks as part of the IWSLT18 evaluation campaign.

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.

This report summarizes the MITLL-AFRL MT and ASR systems and the experiments run during the 2016 IWSLT evaluation campaign. Building on lessons learned from previous years’ results, we refine our ASR systems and examine the explosion of neural machine translation systems and techniques developed in the past year. We experiment with a variety of phrase-based, hierarchical and neural-network approaches in machine translation and utilize system combination to create a composite system with the best characteristics of all attempted MT approaches.

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.

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.

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.