We describe the Arabic-English and English-Arabic statistical machine translation systems developed by the Qatar Computing Research Institute for the IWSLT’2013 evaluation campaign on spoken language translation. We used one phrase-based and two hierarchical decoders, exploring various settings thereof. We further experimented with three domain adaptation methods, and with various Arabic word segmentation schemes. Combining the output of several systems yielded a gain of up to 3.4 BLEU points over the baseline. Here we also describe a specialized normalization scheme for evaluating Arabic output, which was adopted for the IWSLT’2013 evaluation campaign.

@inproceedings{sajjad:2013:qcri, abstract = {We describe the Arabic-English and English-Arabic statistical machine translation systems developed by the Qatar Computing Research Institute for the IWSLT’2013 evaluation campaign on spoken language translation. We used one phrase-based and two hierarchical decoders, exploring various settings thereof. We further experimented with three domain adaptation methods, and with various Arabic word segmentation schemes. Combining the output of several systems yielded a gain of up to 3.4 BLEU points over the baseline. Here we also describe a specialized normalization scheme for evaluating Arabic output, which was adopted for the IWSLT’2013 evaluation campaign.}, address = {Heidelberg, Germany}, author = {Sajjad, Hassan and Guzm{\'a}n, Francisco and Nakov, Preslav and Abdelali, Ahmed and Murray, Kenton and Al Obaidli, Fahad and Vogel, Stephan}, booktitle = {Proceedings of the 10th International Workshop on Spoken Language Translation {(IWSLT'13)}}, month = {December}, title = {{QCRI} at {IWSLT} 2013: Experiments in Arabic-English and English-Arabic Spoken Language Translation}, volume = {13}, year = {2013} }