Maximal Lattice Overlap in Example-Based Machine Translation

Example-Based Machine Translation (EBMT) retrieves pre-translated phrases from a sentence-aligned bilingual training corpus to translate new input sentences. EBMT uses long pre-translated phrases effectively but is subject to disfluencies at phrasal translation boundaries. We address this problem by introducing a novel method that exploits overlapping phrasal translations and the increased confidence in translation accuracy they imply. We specify an efficient algorithm for producing translations using overlap. Finally, our empirical analysis indicates that this approach produces higher quality translations than the standard method of EBMT in a peak-to-peak comparison. Email: [email protected], [email protected], [email protected], [email protected], [email protected] Work supported in part by the National Science Foundation under grant number IIS-9982226 (MLIAM: MUCHMORE: Multilingual Concept Hierarchies for Medical Information Organization and Retrieval) and award number IIS-9873009 (KDI: Universal Information Access: Translingual Retrieval, Summarization, Tracking, Detection and Validation).