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The goal of machine translation is to translate a sentence S originally generated in a source language into a sentence T in target language. Traditionally in machine translation (and in particular in Statistical Machine Translation) large parallel corpora are gathered and used to create inventories of sub-sentenial units and these, in turn, are combined to create the hypothesis sentence T in the target language that has the maximum likelihood given S. This approach is very flexible as it has the advantage generating reasonable hypotheses even when the input has not resemblance with the training data. However, the most significant disadvantage of Machine Translation is the risk of generating sentences with unnaceptable linguistic (i.e., syntactic, grammatical or pragmatic) incosistences and imprefections. Because of this potential problem and because of the availability of large parallel corpora, MT researchers have recently begun to expore the direct search approach using these translation databases in support of Machine Translation. In these approaches, the underlying assumption is that if an input sentence (which we call a query) S is sufficiently similar to a previously hand translated sentence in the memory, it is, in general, preferable to use such existing translations over the generated Machine Translation hypothesis. For this approach to be practical there needs to exist a sufficiently large database, and it should be possible to identify and retrieve this translation in in a span of time comparable to what it takes for the Machine Translation engine to carry out its task. Hence, the need of algorithms to efficiently search these large databases. In this work we focus on a novel approach to Machine Translation memory lookup based on the efficient and incremental computation of the string edit distance. The string edit distance (SED) between two strings is defined as the number of operations (i.e., insertions, deletions and substitutions) that need to be applied on one string in order to transform it into the second one (Wagner & Fischer, 1974). The SED is a symmetric operation. To be more precise, our approach leverages the rapid elimination of unpromising candidates using increasingly stringent elimination criteria. Our approach guarantees an optimal answer as long as this answer has an SED from the query smaller than a user defined threshold. In the next section we first introduct string similarity translation memory search, specifically based on the string edit distance computation, and following we present our approach which focuses on speeding up the translation memory search using increasingly stringent sentence signatures. We then describe how to implement our approach using a Map/Reduce framework and we conclude with experiments that illustrate the advantages of our method.

Towards Efficient Translation Memory Search Based on Multiple Sentence Signatures