Abstract
Grammatical Error Correction systems are typically evaluated overall, without taking into consideration performance on individual error types because system output is not annotated with respect to error type. We introduce a tool that automatically classifies errors in Russian learner texts. The tool takes an edit pair consisting of the original token(s) and the corresponding replacement and provides a grammatical error category. Manual evaluation of the output reveals that in more than 93% of cases the error categories are judged as correct or acceptable. We apply the tool to carry out a fine-grained evaluation on the performance of two error correction systems for Russian.- Anthology ID:
- 2022.lrec-1.605
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 5637–5647
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.605
- DOI:
- Cite (ACL):
- Alla Rozovskaya. 2022. Automatic Classification of Russian Learner Errors. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 5637–5647, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Automatic Classification of Russian Learner Errors (Rozovskaya, LREC 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.lrec-1.605.pdf