Improving statistical translation through editing

26 Improved Statistical Translation Through Editing Chris Callison-Burch, Colin Bannard and Josh Schroeder School of Informatics Linear B Ltd. University of Edinburgh Technology Transfer Centre 2 Buccleuch Place King’s Buildings Edinburgh EH8 9LW Edinburgh EH9 3JL {chris,colin}@linearb.co.uk josh@linearb.co.uk 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. Introduction Statistical machine translation was first proposed in Brown et al (1988). Since statistical machine translation systems are created by automatically analyzing a corpus of example translations they have a number of advantages over systems that are built using more traditional approaches to MT: • They make few linguistic assumptions and can therefore be applied to nearly any language pair, given a sufficiently large corpus. • They can be developed in a matter of weeks or days, whereas systems that are hand-crafted by linguists and lexicographers can take years. • They can be improved with little additional effort as more data becomes available. More recent advances in phrase-based approaches to statistical translation (Koehn et al (2003), Marcu and Wong (2002), Och et al (1999)) have led to a dramatic increase in the quality of the translation systems. Phrase-based translation systems produce higher-quality translation since they use longer segments of human translated text. Using longer segments of human translated text reduces problems associated with literal word-for-word translations. For example, multi-word expressions such as idioms are better translated. Linear B is a commercial provider of statistical machine translation systems. This paper describes Linear B's advances to phrase-based machine translation that allow translation quality to be improved through editing translations that are produced by our system. 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. Our system improves by dynamically learning the correct translations of new phrases. These new phrases are extracted from the corrected sentence pair using the existing translation models, and can be used immediately for subsequent translations. • The second is through a mechanism that allows an advanced user to inspect which phrases the system's translation was composed from. If a particular phrase was mistranslated, the user can examine the set of sentence pairs that a particular translation was learned from, and make corrections. These features mean that our system is capable of improving with use and adapting to be more appropriate for a new domain. This …

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