Error Driven Paraphrase Annotation using Mechanical Turk

The source text provided to a machine translation system is typically only one of many ways the input sentence could have been expressed, and alternative forms of expression can often produce a better translation. We introduce here error driven paraphrasing of source sentences: instead of paraphrasing a source sentence exhaustively, we obtain paraphrases for only the parts that are predicted to be problematic for the translation system. We report on an Amazon Mechanical Turk study that explores this idea, and establishes via an oracle evaluation that it holds the potential to substantially improve translation quality.