A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction
Table 3: Performance of various architectures on the CoNLL-2013 test set.
['Architecture', 'Prec.', 'Recall', 'F0.5']
BiLSTM can capture the entire sentence context from left and right for each input word, whereas SLConv captures only a few surrounding words (equal to the filter width of 3). However, MLConv captures a larger surrounding context (7 layers × filter width 3=21 tokens) more effectively, causing it to outperform both SLConv and BiLSTM.