Improving statistical translation through editing.
Chris Callison-Burch, Colin Bannard, Josh E. Schroeder · 2004
Abstract. In this paper we introduce Linear B's statistical machine translation system. We describe how Linear B's phrase-based translation models are learned from a parallel corpus, and show how the quality of the translations produced by our system can be improved over time through editing. There are two levels at which our translations can be edited. The first is through a simple correction of the text that is produced by our system. The second is through a mechanism which allows an advanced user to examine the sentences that a particular translation was learned from. The learning process can be improved by correcting which phrases in the sentence should be considered translations of each other. 1.