Abstract
The term translation spotting (TS) refers to the task of identifying the target-language (TL) words that correspond to a given set of source-language (SL) words in a pair of text segments known to be mutual translations. This article examines this task within the context of a sub-sentential translation-memory system, i.e. a translation support tool capable of proposing translations for portions of a SL sentence, extracted from an archive of existing translations. Different methods are proposed, based on a statistical translation model. These methods take advantage of certain characteristics of the application, to produce TL segments submitted to constraints of contiguity and compositionality. Experiments show that imposing these constraints allows important gains in accuracy, with regard to the most probable alignments predicted by the model.
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