DCU-Lingo24 Participation in WMT 2014 Hindi-English Translation task
By Xiaofeng Wu, Rejwanul Haque, Tsuyoshi Okita, Andy Way, Qun Liu. 2014
This paper describes the DCU-Lingo24 submission to WMT 2014 for the HindiEnglish translation task. We exploit miscellaneous methods in our system, including: Context-Informed PB-SMT, OOV Word Conversion (OWC), MultiAlignment Combination (MAC), Operation Sequence Model (OSM), Stemming Align and Normal Phrase Extraction (SANPE), and Language Model Interpolation (LMI). We also describe various preprocessing steps we tried for Hindi in this task.
Subfields: Natural Language Processing
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In Natural Language Processing
- Philipp Koehn
- Lucia Specia
- Ondrej Bojar
- Barry Haddow
- Matt Post
- Christof Monz
- Rico Sennrich
- Graham Neubig
Citations in the corpus
- Findings of the 2014 Workshop on Statistical Machine Translation (2014, background): “While the precise metric has varied over the years, it has always shared a common idea of computing the average number of times each system was judged better than other systems, and ranking from highest to lowest.”