The AFRL WMT19 Systems: Old Favorites and New Tricks
This paper describes the Air Force Research Laboratory (AFRL) machine translation systems and the improvements that were developed during the WMT19 evaluation campaign. This year, we refine our approach to training popular neural machine translation toolkits, experiment with a new domain adaptation technique and again measure improvements in performance on the Russian{--}English language pair.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationMachine TranslationTranslationSimilar Papers 제목 키워드 기반
The AFRL IWSLT 2018 Systems: What Worked, What Didn’t
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…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Machine Translationspeech-recognition+2The AFRL-Ohio State WMT18 Multimodal System: Combining Visual with Traditional
AFRL-Ohio State extends its usage of visual domain-driven machine translation for use as a peer with traditional machine translation systems. As a peer, it is enveloped into a system combination of neural and statistical…
Machine TranslationMultimodal Machine TranslationTranslationThe AFRL WMT18 Systems: Ensembling, Continuation and Combination
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 additio…
Machine TranslationTranslationThe AFRL WMT20 News Translation Systems
This report summarizes the Air Force Research Laboratory (AFRL) machine translation (MT) systems submitted to the news-translation task as part of the 2020 Conference on Machine Translation (WMT20) evaluation campaign. T…
Machine TranslationTranslationTune in: The AFRL WMT21 News-Translation Systems
This paper describes the Air Force Research Laboratory (AFRL) machine translation sys- tems and the improvements that were developed during the WMT21 evaluation campaign. This year, we explore various methods of adapting…
Machine TranslationTranslation