Machine Translation and Monolingual Postediting: The AFRL WMT-14 System
By Lane Schwartz, Tim Anderson, Jeremy Gwinnup, Katherine Young. 2014
17 citations recorded by the source corpus.
This paper describes the AFRL statistical MT system and the improvements that were developed during the WMT14 evaluation campaign. As part of these efforts we experimented with a number of extensions to the standard phrase-based model that improve performance on Russian to English and Hindi to English translation tasks. In addition, we describe our efforts to make use of monolingual English speakers to correct the output of machine translation, and present the results of monolingual postediting of the entire 3003 sentences of the WMT14 Russian-English test set.
Subfields: Artificial Intelligence, Machine Translation
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In Artificial Intelligence
- Jeremy Gwinnup
- Brian Thompson
- Katherine Young
- Tim Anderson
- Michaeel Kazi
- Elizabeth Salesky
- Grant Erdmann
- Felix Stahlberg
In Machine Translation
Citations in the corpus
- Findings of the 2014 Workshop on Statistical Machine Translation (2014, neutral): “AFRL, AFRL-PE Air Force Research Lab (Schwartz et al., 2014)”
- The AFRL-MITLL WMT15 System: There’s More than One Way to Decode It! (2015, methodology): “As with our WMT14 submission systems, preprocessing to address issues with the training data was required to ensure optimal system performance.”