Maximal Lattice Overlap in Example-Based Machine Translation
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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 addres s 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.