This paper proposes a simple and effective algorithm for incorporating lexical constraints in neural machine translation.
We also show that a state-of-the-art GEC system can be improved when quality scores are used as features for re-ranking the N-best candidates.
Ranked #2 on Grammatical Error Correction on Restricted
Previous studies of the correlation of these metrics with human quality judgments were inconclusive, due to the lack of appropriate significance tests, discrepancies in the methods, and choice of datasets used.
We improve automatic correction of grammatical, orthographic, and collocation errors in text using a multilayer convolutional encoder-decoder neural network.
Ranked #1 on Grammatical Error Correction on Restricted
We build a grammatical error correction (GEC) system primarily based on the state-of-the-art statistical machine translation (SMT) approach, using task-specific features and tuning, and further enhance it with the modeling power of neural network joint models.
Grammatical error correction (GEC) is the task of detecting and correcting grammatical errors in texts written by second language learners.
Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy.