Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction
Table 1: Experimental results on the development set excluding the held-out 10k tuning subset.
['Model', 'Data', 'Precision', 'Recall', '[ITALIC] F1']
Here (and elsewhere) Random is the result of randomly assigning a sentence to one of the binary classes. For the CNN classifiers, fine-tuning the word2vec embeddings improves performance. The encoder-decoder models improve over the CNN classifiers, even though the latter are provided with additional data (via word2vec). The character-based models yield tangible improvements over the word-based models.