Create README.md

README.md

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+---
+language:
+- cs
+license: cc-by-nc-sa-4.0
+---
+## Introduction
+This dataset is extracted by postprocessing data from Náplava et al., 2019. Specificially, we extract gramatically incorrect sentences, and their respective corrections.
+Then we convert task to binary detection of errorneous sentences. We downloaded the original dataset from [LINDAT-Clarin repository](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3057).
+
+
+## Citation
+```bibtex
+@inproceedings{naplava-straka-2019-grammatical,
+    title = "Grammatical Error Correction in Low-Resource Scenarios",
+    author = "N{\'a}plava, Jakub  and
+      Straka, Milan",
+    editor = "Xu, Wei  and
+      Ritter, Alan  and
+      Baldwin, Tim  and
+      Rahimi, Afshin",
+    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D19-5545",
+    doi = "10.18653/v1/D19-5545",
+    pages = "346--356",
+    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 \url{http://hdl.handle.net/11234/1-3057}, and the source code of the GEC model is available at \url{https://github.com/ufal/low-resource-gec-wnut2019}.",
+}
+```