Self-feeding Semi-supervised Training Method for Grammatical Error Correction

Abstract

Grammatical error correction (GEC) has been successful with deep and complex neural machine translation models, but published annotated datasets to train the large models are scarce. In this dissertation, I propose a novel self-feeding training method that generates incorrect sentences from correct sentences. The proposed training method can generate appropriate wrong sentences from unlabeled sentences, using a data generation model trained as an autoencoder. It can also add artificial noise to correct sentences to automatically generate noisy sentences. I show that the GEC models trained with the self-feeding training method are successful without extra annotated data or deeper neural network-based models, achieving F0.5 score of 0.5982 on the CoNLL-2014 Shared Task test data with a transformer model. The results also show that fully unlabeled training is possible for data-scarce domains and languages.