More specifically, we would like to select the candidate sentence that has the smallest LD from the binary sequence created by the detection model: 1 True and false positives: Given the binary sequences described above, we also use as a feature the ratio of true positives (TP) to false positives (FP) by treating the error detection model as the "gold standard".
we count how many times the candidate hypothesis agrees or not with the detection model on the tokens identified as incorrect: TP We use a linear combination of the above three scores together with the overall score (i.e., original rank) given by the SMT system (we do not include any other SMT features) to re-rank each SMT system's 10-best list in an unsupervised way.