Data Issues in English-to-Hindi Machine Translation

Statistical machine translation to morphologically richer languages is a challenging task and more so if the source and target languages differ in word order. Current state-of-the-art MT systems thus deliver mediocre results. Adding more parallel data often helps improve the results; if it doesn't, it may be caused by various problems such as different domains, bad alignment or noise in the new data. In this paper we evaluate the English-to-Hindi MT task from this data perspective. We discuss several available parallel data sources and provide crossevaluation results on their combinations using two freely available statistical MT systems. We demonstrate various problems encountered in the data and describe automatic methods of data cleaning and normalization. We also show that the contents of two independently distributed data sets can unexpectedly overlap, which negatively affects translation quality. Together with the error analysis, we also present a new tool for viewing align...

• Type: Conference
• Year: 2010
• Where: LREC
• Authors: Ondrej Bojar, Pavel Stranák, Daniel Zeman
• Keywords: Education, LREC 2010, MT Systems, Parallel Data, Target Language