TransDoop: A Map-Reduce based Crowdsourced Translation for Complex Domain

Large amount of parallel corpora is required for building Statistical Machine Translation (SMT) systems. We describe the TransDoop system for gathering translations to create parallel corpora from online crowd workforce who have familiarity with multiple languages but are not expert translators. Our system uses a Map-Reduce-like approach to translation crowdsourcing where sentence translation is decomposed into the following smaller tasks: (a) translation of constituent phrases of the sentence; (b) validation of quality of the phrase translations; and (c) composition of complete sentence translations from phrase translations. TransDoop incorporates quality control mechanisms and easy-to-use worker user interfaces designed to address issues with translation crowdsourcing. We have evaluated the crowd’s output using the METEOR metric. For a complex domain like judicial proceedings, the higher scores obtained by the map-reduce based approach compared to complete sentence translation establishes the efficacy of our work.