@inproceedings{flachs-etal-2019-noisy,
title = "Noisy Channel for Low Resource Grammatical Error Correction",
author = "Flachs, Simon and
Lacroix, Oph{\'e}lie and
S{\o}gaard, Anders",
editor = "Yannakoudakis, Helen and
Kochmar, Ekaterina and
Leacock, Claudia and
Madnani, Nitin and
Pil{\'a}n, Ildik{\'o} and
Zesch, Torsten",
booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4420",
doi = "10.18653/v1/W19-4420",
pages = "191--196",
abstract = "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.",
}
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%0 Conference Proceedings
%T Noisy Channel for Low Resource Grammatical Error Correction
%A Flachs, Simon
%A Lacroix, Ophélie
%A Søgaard, Anders
%Y Yannakoudakis, Helen
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Zesch, Torsten
%S Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F flachs-etal-2019-noisy
%X 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.
%R 10.18653/v1/W19-4420
%U https://aclanthology.org/W19-4420
%U https://doi.org/10.18653/v1/W19-4420
%P 191-196
Markdown (Informal)
[Noisy Channel for Low Resource Grammatical Error Correction](https://aclanthology.org/W19-4420) (Flachs et al., BEA 2019)
ACL
- Simon Flachs, Ophélie Lacroix, and Anders Søgaard. 2019. Noisy Channel for Low Resource Grammatical Error Correction. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 191–196, Florence, Italy. Association for Computational Linguistics.