Candidate re-ranking for SMT-based grammatical error correction

We develop a supervised ranking model to rerank candidates generated from an SMT-based grammatical error correction (GEC) system. A range of novel features with respect to GEC are investigated and implemented in our reranker. We train a rank preference SVM model and demonstrate that this outperforms both Minimum Bayes-Risk and Multi-Engine Machine Translation based re-ranking for the GEC task. Our best system yields a significant improvement in I-measure when testing on the publicly available FCE test set (from 2.87% to 9.78%). It also achieves an F0.5 score of 38.08% on the CoNLL-2014 shared task test set, which is higher than the best original result. The oracle score (upper bound) for the re-ranker achieves over 40% I-measure performance, demonstrating that there is considerable room for improvement in the re-ranking component developed here, such as incorporating features able to capture long-distance dependencies.