Symmetric Probabilistic Alignment for Example-Based Translation

Abstract. Since subsentential alignment is critically important to the translation quality of an Example-Based Machine Translation (EBMT) system which operates by finding and combining phrase-level matches against the training examples, we recently decided to de-velop a new alignment algorithm for the purpose of improving the EBMT system’s per-formance. Unlike most algorithms in the literature, this new Symmetric Probabilistic Align-ment (SPA) algorithm treats the source and target languages in a symmetric fashion. In this paper, we describe our basic algorithm and some extensions for using context and posi-tional information, compare its alignment accuracy with IBM Model 4, and report on ex-periments in which either IBM Model 4 or SPA alignments are substituted for the aligner currently built into the EBMT system. Both Model 4 and SPA are significantly better than the internal aligner and SPA slightly outperforms Model 4 despite being handicapped by incomplete integration with EBMT. 1