Noisy Channel for Low Resource Grammatical Error Correction
Simon Flachs, Ophélie Lacroix, Anders Søgaard · 2019
This paper describes our contribution to the low-resource track of the BEA 2019 shared task on Grammatical Error Correction (GEC).Our approach to GEC builds on the theory of the noisy channel by combining a channel model and language model.We generate confusion sets from the Wikipedia edit history and use the frequencies of edits to estimate the channel model.Additionally, we use two pretrained language models: 1) Google's BERT model, which we fine-tune for specific error types and 2) OpenAI's GPT-2 model, utilizing that it can operate with previous sentences as context.Furthermore, we search for the optimal combinations of corrections using beam search.