Bloodgood, MichaelStrauss, BenjaminTranslation Memory (TM) systems are one of the most widely used translation technologies. An important part of TM systems is the matching algorithm that determines what translations get retrieved from the bank of available translations to assist the human translator. Although detailed accounts of the matching algorithms used in commercial systems can’t be found in the literature, it is widely believed that edit distance algorithms are used. This paper investigates and evaluates the use of several matching algorithms, including the edit distance algorithm that is believed to be at the heart of most modern commercial TM systems. This paper presents results showing how well various matching algorithms correlate with human judgments of helpfulness (collected via crowdsourcing with Amazon’s Mechanical Turk). A new algorithm based on weighted n-gram precision that can be adjusted for translator length preferences consistently returns translations judged to be most helpful by translators for multiple domains and language pairs.en-UScomputer sciencestatistical methodscomputational linguisticsinformation retrievalnatural language processinghuman language technologytranslation technologycomputer-aided translationcomputer-assisted translationCAT toolstranslation memory systemstranslation memory retrieval methodsAmazon Mechanical Turkmatching algorithmsfuzzy matchfuzzy match algorithmsfuzzy match scorepercent matchweighted percent matchedit distancen-gram precisionweighted n-gram precisionmodified weighted n-gram precisiontranslation match score thresholdfuzzy match thresholdfuzzy match score thresholdmatch length preferencestranslation match length preferencesArticle