Candidate re-ranking for SMT-based grammatical error correctionCandidate re-ranking for SMT-based grammatical error correctionYuan, Zheng and Briscoe, Ted and Felice, Mariano2016
Paper summarymarekThey improve an existing error correction system by re-ranking its predictions. The basic approach uses machine translation to perform error correction on learner texts – the incorrect text is essentially translated into correct text. Here, they include a ranking SVM to score and reorder the n-best lists from the translation model.
The reranking features include various internal scores from the translation model, the rank in the original ordering, language model probabilities trained on large corpora, language model scores based on only the n-best list, word-level translation probabilities, and sentence length features. They show improvement on two error correction datasets.
https://i.imgur.com/RxAE11a.png
Example output from the models.
They improve an existing error correction system by re-ranking its predictions. The basic approach uses machine translation to perform error correction on learner texts – the incorrect text is essentially translated into correct text. Here, they include a ranking SVM to score and reorder the n-best lists from the translation model.
The reranking features include various internal scores from the translation model, the rank in the original ordering, language model probabilities trained on large corpora, language model scores based on only the n-best list, word-level translation probabilities, and sentence length features. They show improvement on two error correction datasets.
https://i.imgur.com/RxAE11a.png
Example output from the models.