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Inference of Phrase-Based Translation Models via Minimum Description Length. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2014. pp. 90-94. Association for Computational Linguistics.We present an unsupervised inference procedure for phrase-based translation models based on the minimum description length principle. In comparison to current inference techniques that rely on long pipelines of training heuristics, this procedure represents a theoretically well-founded approach to directly infer phrase lexicons. Empirical results show that the proposed inference procedure has the potential to overcome many of the problems inherent to the current inference approaches for phrase-based models.