Grammatical Error Correction in Low-Resource Scenarios

October 01, 2019 · Entered Twilight · 🏛 Conference on Empirical Methods in Natural Language Processing

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Authors Jakub Náplava, Milan Straka arXiv ID 1910.00353 Category cs.CL: Computation & Language Citations 66 Venue Conference on Empirical Methods in Natural Language Processing Repository https://github.com/ufal/low-resource-gec-wnut2019 ⭐ 13 Last Checked 1 month ago

Abstract

Grammatical error correction in English is a long studied problem with many existing systems and datasets. However, there has been only a limited research on error correction of other languages. In this paper, we present a new dataset AKCES-GEC on grammatical error correction for Czech. We then make experiments on Czech, German and Russian and show that when utilizing synthetic parallel corpus, Transformer neural machine translation model can reach new state-of-the-art results on these datasets. AKCES-GEC is published under CC BY-NC-SA 4.0 license at https://hdl.handle.net/11234/1-3057 and the source code of the GEC model is available at https://github.com/ufal/low-resource-gec-wnut2019.