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Daniel Ortiz-Martínez, Ismael García-Varea, Francisco Casacuberta. Phrase-level alignment generation using a smoothed loglinear phrase-based statistical alignment model. Proc. of EAMT'08, 2008.

We present a phrase-based statistical alignment model togheter with a set of different smoothing techniques to be applied when the best phrase-to-phrase alignment for a pair of sentences is to be computed. We follow a loglinear approach, which allows us to introduce different scoring functions to control specific aspects of phrase-level alignments. Experimental results for a well-known shared task on word alignment evaluation are reported, showing the great importance of smoothing in the generation of alignments. As a step forward, we also discuss the adaptation of the proposed model for its use in a CAT (Computer Assisted Translation) system.