Noisy Channel for Low Resource Grammatical Error Correction
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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 pre-trained 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.
Original language | English |
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Title of host publication | Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications |
Publisher | Association for Computational Linguistics |
Publication date | 2019 |
Pages | 191-196 |
DOIs | |
Publication status | Published - 2019 |
Event | 14th Workshop on Innovative Use of NLP for Building Educational Applications - Florence, Italy Duration: 2 Aug 2019 → … |
Workshop
Workshop | 14th Workshop on Innovative Use of NLP for Building Educational Applications |
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By | Florence, Italy |
Periode | 02/08/2019 → … |
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