Department of Computer Engineering2024-11-1020119781-9372-8412-1N/A2-s2.0-84994697685N/Ahttps://hdl.handle.net/20.500.14288/17197We present the results we obtain using our RegMT system, which uses transductive regression techniques to learn mappings between source and target features of given parallel corpora and use these mappings to generate machine translation outputs. Our training instance selection methods perform feature decay for proper selection of training instances, which plays an important role to learn correct feature mappings. RegMT uses L2 regularized regression as well as L1 regularized regression for sparse regression estimation of target features. We present translation results using our training instance selection methods, translation results using graph decoding, system combination results with RegMT, and performance evaluation with the F1 measure over target features as a metric for evaluating translation quality.Computer engineeringConference proceedinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84994697685andpartnerID=40andmd5=6abf723d4ffa537a3ea7762a3f39c19eN/A9833