Noisy Channel for Low Resource Grammatical Error Correction
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning
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Noisy Channel for Low Resource Grammatical Error Correction. / Flachs, Simon; Lacroix, Ophélie; Søgaard, Anders.
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, 2019. s. 191-196.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning
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TY - GEN
T1 - Noisy Channel for Low Resource Grammatical Error Correction
AU - Flachs, Simon
AU - Lacroix, Ophélie
AU - Søgaard, Anders
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
U2 - 10.18653/v1/W19-4420
DO - 10.18653/v1/W19-4420
M3 - Article in proceedings
SP - 191
EP - 196
BT - Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
PB - Association for Computational Linguistics
Y2 - 2 August 2019
ER -
ID: 240410261