The Impact of Arabic Morphological Segmentation on Broad-coverage English-to-Arabic Statistical Machine Translation

Hassan Al-Haj, Alon Lavie


Abstract
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 full spectrum 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 2.61 BLEU points between the best and worst segmentation schemes indicating that the choice of the segmentation scheme has a significant effect on the performance of a PBSMT system in a large data scenario. We also show that a simple segmentation scheme can perform as good as the best and more complicated segmentation scheme. We also report results on a wide set of techniques for recombining the segmented Arabic output.
Anthology ID:
2010.amta-papers.4
Volume:
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
Month:
October 31-November 4
Year:
2010
Address:
Denver, Colorado, USA
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
Language:
URL:
https://aclanthology.org/2010.amta-papers.4
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2010.amta-papers.4.pdf