A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction
Table 4: Results of different embedding initializations on the CoNLL-2013 test set.
['Initialization', 'Prec.', 'Recall', 'F0.5']
We assess various methods of initializing the source and target word embeddings. We train skip-gram models with word2vec and use parameters identical to those we use for fastText. fastText embeddings have access to the character sequences that make up the words and hence are better suited to learn word representations taking morphology into account. We also find that initializing with fastText works well empirically, and hence we choose these embeddings to initialize our network when evaluating on benchmark test datasets.