- Houda Bouamor, Hassan Sajjad, Nadir Durrani, Kemal Oflazer. 2015. QCMUQ@QALB-2015 Shared Task: Combining Character level MT and Error-tolerant Finite-State Recognition for Arabic Spelling Correction. Abstract: We describe the CMU-Q and QCRI’s joint efforts in building a spelling correction system for Arabic in the QALB 2015 Shared Task. Our system is based on a hybrid pipeline that combines rule-based linguistic techniques with statistical meth-ods using language modeling and machine translation, as well as an error-tolerant ﬁnite-state automata method. We trained and tested our spelling corrector using the dataset provided by the shared task orga-nizers. Our system outperforms the base-line system and yeilds better correction quality with an F-score of 68.12 on L1-test-2015 testset and 38.90 on the L2-test-2015. This ranks us 2nd in the L2 subtask and 5th in the L1 subtask.
