Czech Grammar Error Correction with a Large and Diverse Corpus

Author:

Náplava Jakub1,Straka Milan2,Straková Jana3,Rosen Alexandr4

Affiliation:

1. Charles University, Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics, Czech Republic. naplava@ufal.mff.cuni.cz

2. Charles University, Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics, Czech Republic. straka@ufal.mff.cuni.cz

3. Charles University, Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics, Czech Republic. strakova@ufal.mff.cuni.cz

4. Charles University, Faculty of Arts Institute of Theoretical and Computational Linguistics, Czech Republic. alexandr.rosen@ff.cuni.cz

Abstract

Abstract We introduce a large and diverse Czech corpus annotated for grammatical error correction (GEC) with the aim to contribute to the still scarce data resources in this domain for languages other than English. The Grammar Error Correction Corpus for Czech (GECCC) offers a variety of four domains, covering error distributions ranging from high error density essays written by non-native speakers, to website texts, where errors are expected to be much less common. We compare several Czech GEC systems, including several Transformer-based ones, setting a strong baseline to future research. Finally, we meta-evaluate common GEC metrics against human judgments on our data. We make the new Czech GEC corpus publicly available under the CC BY-SA 4.0 license at http://hdl.handle.net/11234/1-4639.

Publisher

MIT Press - Journals

Subject

Artificial Intelligence,Computer Science Applications,Linguistics and Language,Human-Computer Interaction,Communication

Reference58 articles.

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhanced Grammar Error Detection and Correction Using Hybrid Algorithm;2023 Innovations in Power and Advanced Computing Technologies (i-PACT);2023-12-08

2. Multigranularity Pruning Model for Subject Recognition Task under Knowledge Base Question Answering When General Models Fail;International Journal of Intelligent Systems;2023-10-30

3. Grammatical Error Correction: A Survey of the State of the Art;Computational Linguistics;2023-07-10

4. Research on the Application of Neural Network Classification Model in English Grammar Error Correction;ACM Transactions on Asian and Low-Resource Language Information Processing;2023-05-08

5. Construction of an Error-Tagged Evaluation Corpus for Japanese Grammatical Error Correction;Journal of Natural Language Processing;2023

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