Manawi: Using multi-word expressions and named entities to improve machine translation

Abstract

We describe the Manawi1 (mAnEv) system submitted to the 2014 WMT translation shared task. We participated in the English-Hindi (EN-HI) and Hindi-English (HI-EN) language pair and achieved 0.792 for the Translation Error Rate (TER) score2 for EN-HI, the lowest among the competing systems. Our main innovations are (i) the usage of outputs from NLP tools, viz. billingual multi-word expression extractor and named-entity recognizer to improve SMT quality and (ii) the introduction of a novel filter method based on sentence-Alignment features. The Manawi system showed the potential of improving translation quality by incorporating multiple NLP tools within the MT pipeline.