Handling Indonesian Clitics: A Dataset Comparison for an Indonesian-English Statistical Machine Translation System

In this paper, we study the effect of incorporating morphological information on an Indonesian (id) to English (en) Statistical Machine Translation (SMT) system as part of a preprocessing module. The linguistic phenomenon that is being addressed here is Indonesian cliticized words. The approach is to transform the text by separating the correct clitics from a cliticized word to simplify the word alignment. We also study the effect of applying the preprocessing on different SMT systems trained on different kinds of text, such as spoken language text. The system is built using the state-of-the-art SMT tool, MOSES. The Indonesian morphological information is provided by MorphInd. Overall the preprocessing improves the translation quality, especially for the Indonesian spoken language text, where it gains 1.78 BLEU score points of increase.