Laurie Gerber

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The systems built in these projects exhibit a fairly standard structure: they create a query from the user’s question, perform IR with the query to locate (segments of) documents likely to contain an answer, and then pinpoint the most likely answer passage within the candidate documents. The most common difference lies in the pinpointing. Many projects(More)
We focus on developing an account of user behavior under error conditions, working with annotated data from real human-machine mixed initiative dialogs. In particular, we examine categories of error perception, user behavior under error, effect of user strategies on error recovery, and the role of user initiative in error situations. A conditional(More)
SYSTRAN has demonstrated success in the MT field with its long history spanning nearly 30 years. As a general-purpose fully automatic MT system, SYSTRAN employs a transfer approach. Among its several components, large, carefully encoded, high-quality dictionaries are critical to SYSTRAN's translation capability. A total of over 2.4 million words and(More)
We describe a case study that presents a framework for examining whether Machine Translation (MT) output enables translation professionals to translate faster while at the same time producing better quality translations than without MT output. We seek to find decision factors that enable a translation professional, known as a Paralinguist, to determine(More)
In this paper, we describe the methods used to develop an exchangeable translation memory bank of sentence-aligned Mandarin Chinese English sentences. This effort is part of a larger effort, initiated by the National Virtual Translation Center (NVTC), to foster collaboration and sharing of translation memory banks across the Intelligence Community and the(More)
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