ParFDA for Instance Selection for Statistical Machine Translation
By Ergun Biçici. 2016
8 citations recorded by the source corpus.
We build parallel feature decay algorithms (ParFDA) Moses statistical machine translation (SMT) systems for all language pairs in the translation task at the first conference on statistical machine translation (Bojar et al., 2016a) (WMT16). ParFDA obtains results close to the top constrained phrase-based SMT with an average of 2.52 BLEU points difference using significantly less computation for building SMT systems than the computation that would be spent using all available corpora. We obtain BLEU bounds based on target coverage and show that ParFDA results can be improved by 12.6 BLEU points on average. Similar bounds show that top constrained SMT results at WMT16 can be improved by 8 BLEU points on average while German to English and Romanian to English translations results are already close to the bounds. 1 ParFDA
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Machine Translation
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In Artificial Intelligence
In Computer Vision and Pattern Recognition
In Machine Translation
- Ergun Biçici
- Andy Way
- Lucia Specia
- Felipe Sánchez-Martínez
- Miquel Esplà-Gomis
- Mikel L. Forcada
- Qun Liu
- Kashif Shah
Citations in the corpus
- Findings of the 2016 Conference on Machine Translation (2016, neutral): “PARFDA Ergun Bicici (Bicici, 2016a)”
- Predicting Translation Performance with Referential Translation Machines (2017, methodology): “RTMs use ParFDA (Bic¸ici, 2016a) for instance selection and machine translation performance prediction sys-tem (MTPPS) (Bic¸ici and Way, 2015) ing features where the total number of features becomes 514, increasing depending on the order of n -grams used and we used up to 5 -grams for translation…”
- RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity (2017, methodology): “RTMs use ParFDA (Biçici, 2016a) for instance selection and machine translation performance prediction system (MTPPS) (Biçici and Way, 2015) for generating features for the training and the test set mapping both to the same space where the total number of features in each task becomes 368.”