Maximizing Component Quality in Bilingual Word-Aligned Segmentations

Given a pair of source and target language sentences which are translations of each other with known word alignments between them, we extract bilingual phrase-level segmentations of such a pair. This is done by identifying two appropriate measures that assess the quality of phrase segments, one on the monolingual level for both language sides, and one on the bilingual level. The monolingual measure is based on the notion of partition refinements and the bilingual measure is based on structural properties of the graph that represents phrase segments and word alignments.
These two measures are incorporated in a basic adaptation of the Cross-Entropy method for the purpose of extracting an N-best list of bilingual phrase-level segmentations. A straight-forward application of such lists in Statistical Machine Translation (SMT) yields a conservative phrase pair extraction method that reduces phrase-table sizes by 90% with insignificant loss in translation quality.