Robust parfda Statistical Machine Translation Results
We build parallel feature decay algorithms (parfda) Moses statistical machine translation (SMT) models for language pairs in the translation task. parfda obtains results close to the top constrained phrase-based SMT with an average of 2.252 BLEU points difference on WMT 2017 datasets using significantly less computation for building SMT systems than that would be spent using all available corpora. We obtain BLEU upper bounds based on target coverage to identify which systems used additional data. We use PRO for tuning to decrease fluctuations in the results and postprocess translation outputs to decrease translation errors due to the casing of words. F1 scores on the key phrases of the English to Turkish testsuite that we prepared reveal that parfda achieves 2nd best results. Truecasing translations before scoring obtained the best results overall.
Code (0)
등록된 구현이 없습니다.
Tasks
Language ModelingLanguage ModellingMachine TranslationTranslationSimilar Papers 제목 키워드 기반
Machine Translation with parfda, Moses, kenlm, nplm, and PRO
We build parfda Moses statistical machine translation (SMT) models for most language pairs in the news translation task. We experiment with a hybrid approach using neural language models integrated into Moses. We obtain …
Machine TranslationTranslationParFDA for Instance Selection for Statistical Machine Translation
ParFDA for Instance Selection for Statistical Machine Translation
ParFDA for Fast Deployment of Accurate Statistical Machine Translation Systems, Benchmarks, and Statistics
A Hybrid Model for Enhancing Lexical Statistical Machine Translation (SMT)
The interest in statistical machine translation systems increases currently due to political and social events in the world. A proposed Statistical Machine Translation (SMT) based model that can be used to translate a se…
Language ModelingLanguage ModellingMachine TranslationSentence+1