We present an open-source framework for large-scale online structured learning. Developed with the flexibility to handle cost-augmented inference problems such as statistical machine translation (SMT), our large-margin learner can be used with any decoder. Integration with MapReduce using Hadoop streaming allows efficient scaling with increasing size of training data. Although designed with a focus on SMT, the decoder-agnostic design of our learner allows easy future extension to other structured learning problems such as sequence labeling and parsing.
['Vladimir Eidelman', 'Ke Wu', 'Ferhan Türe', 'Philip Resnik', 'Jimmy J. Lin']
Mr. MIRA: Open-Source Large-Margin Structured Learning on MapReduce