The impact of Arabic morphological segmentation on broad-coverage English-to-Arabic statistical machine translation

Morphologically rich languages pose a challenge for statistical machine translation (SMT). This challenge is magnified when translating into a morphologically rich language. In this work we address this challenge in the framework of a broad-coverage English-to-Arabic phrase based statistical machine translation (PBSMT). We explore the largest-to-date set of Arabic segmentation schemes ranging from full word form to fully segmented forms and examine the effects on system performance. Our results show a difference of 2.31 BLEU points averaged over all test sets between the best and worst segmentation schemes indicating that the choice of the segmentation scheme has a significant effect on the performance of an English-to-Arabic PBSMT system in a large data scenario. We show that a simple segmentation scheme can perform as well as the best and more complicated segmentation scheme. An in-depth analysis on the effect of segmentation choices on the components of a PBSMT system reveals that text fragmentation has a negative effect on the perplexity of the language models and that aggressive segmentation can significantly increase the size of the phrase table and the uncertainty in choosing the candidate translation phrases during decoding. An investigation conducted on the output of the different systems, reveals the complementary nature of the output and the great potential in combining them.