Abstract
This paper describes the BLCU Group submissions to the Building Educational Applications (BEA) 2019 Shared Task on Grammatical Error Correction (GEC). The task is to detect and correct grammatical errors that occurred in essays. We participate in 2 tracks including the Restricted Track and the Unrestricted Track. Our system is based on a Transformer model architecture. We integrate many effective methods proposed in recent years. Such as, Byte Pair Encoding, model ensemble, checkpoints average and spell checker. We also corrupt the public monolingual data to further improve the performance of the model. On the test data of the BEA 2019 Shared Task, our system yields F0.5 = 58.62 and 59.50, ranking twelfth and fourth respectively.- Anthology ID:
- W19-4421
- Volume:
- Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 197–206
- Language:
- URL:
- https://aclanthology.org/W19-4421
- DOI:
- 10.18653/v1/W19-4421
- Cite (ACL):
- Liner Yang and Chencheng Wang. 2019. The BLCU System in the BEA 2019 Shared Task. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 197–206, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- The BLCU System in the BEA 2019 Shared Task (Yang & Wang, BEA 2019)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/W19-4421.pdf
- Data
- Billion Word Benchmark, CoNLL-2014 Shared Task: Grammatical Error Correction, FCE