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Online Multitask Learning for Machine Translation Quality Estimation
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
We present a method for predicting machine translation output quality geared to the needs of computer-assisted translation. These include the capability to: i) continuously learn and self-adapt to a stream of data coming from multiple translation jobs, ii) react to data diversity by exploiting human feedback, and iii) leverage data similarity by learning and transferring knowledge across domains. To achieve these goals, we combine two supervised machine learning paradigms, online and multitaskdoi:10.3115/v1/p15-1022 dblp:conf/acl/SouzaNRT15 fatcat:olw2qzujr5g6naximmjqwm4uoq