Increasing Coverage of Translation Memories with Linguistically Motivated Segment Combination Methods

Translation memories (TMs) used in computer-aided translation (CAT) systems are the highest-quality source of parallel texts since they consist of segment translation pairs approved by professional human translators. The obvious problem is their size and coverage of new document segments when compared with other parallel data. In this paper, we describe several methods for expanding translation memories using linguistically motivated segment combining approaches concentrated on preserving the high translational quality. The evaluation of the methods was done on a medium-size real-world translation memory and documents provided by a Czech translation company as well as on a large publicly available DGT translation memory published by European Commission. The asset of the TM expansion methods were evaluated by the pre-translation analysis of widely used MemoQ CAT system and the METEOR metric was used for measuring the quality of fully expanded new translation segments.