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    <abstract>Translation-based methods for grammar correction that directly map noisy, ungrammatical text to their clean counterparts are able to correct a broad range of errors; however, such techniques are bottlenecked by the need for a large parallel corpus of noisy and clean sentence pairs. In this paper, we consider synthesizing parallel data by noising a clean monolingual corpus. While most previous approaches introduce perturbations using features computed from local context windows, we instead develop error generation processes using a neural sequence transduction model trained to translate clean examples to their noisy counterparts. Given a corpus of clean examples, we propose beam search noising procedures to synthesize additional noisy examples that human evaluators were nearly unable to discriminate from nonsynthesized examples. Surprisingly, when trained on additional data synthesized using our best-performing noising scheme, our model approaches the same performance as when trained on additional nonsynthesized data.</abstract>
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%0 Conference Proceedings
%T Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction
%A Xie, Ziang
%A Genthial, Guillaume
%A Xie, Stanley
%A Ng, Andrew
%A Jurafsky, Dan
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F xie-etal-2018-noising
%X Translation-based methods for grammar correction that directly map noisy, ungrammatical text to their clean counterparts are able to correct a broad range of errors; however, such techniques are bottlenecked by the need for a large parallel corpus of noisy and clean sentence pairs. In this paper, we consider synthesizing parallel data by noising a clean monolingual corpus. While most previous approaches introduce perturbations using features computed from local context windows, we instead develop error generation processes using a neural sequence transduction model trained to translate clean examples to their noisy counterparts. Given a corpus of clean examples, we propose beam search noising procedures to synthesize additional noisy examples that human evaluators were nearly unable to discriminate from nonsynthesized examples. Surprisingly, when trained on additional data synthesized using our best-performing noising scheme, our model approaches the same performance as when trained on additional nonsynthesized data.
%R 10.18653/v1/N18-1057
%U https://aclanthology.org/N18-1057
%U https://doi.org/10.18653/v1/N18-1057
%P 619-628

Markdown (Informal)

[Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction](https://aclanthology.org/N18-1057) (Xie et al., NAACL 2018)

• Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction (Xie et al., NAACL 2018)

ACL

• Ziang Xie, Guillaume Genthial, Stanley Xie, Andrew Ng, and Dan Jurafsky. 2018. Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 619–628, New Orleans, Louisiana. Association for Computational Linguistics.