Abstract
We build a grammatical error correction (GEC) system primarily based on the state-of-the-art statistical machine translation (SMT) approach, using task-specific features and tuning, and further enhance it with the modeling power of neural network joint models. The SMT-based system is weak in generalizing beyond patterns seen during training and lacks granularity below the word level. To address this issue, we incorporate a character-level SMT component targeting the misspelled words that the original SMT-based system fails to correct. Our final system achieves 53.14% F 0.5 score on the benchmark CoNLL-2014 test set, an improvement of 3.62% F 0.5 over the best previous published score.- Anthology ID:
- W17-5037
- Volume:
- Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 327–333
- Language:
- URL:
- https://aclanthology.org/W17-5037
- DOI:
- 10.18653/v1/W17-5037
- Cite (ACL):
- Shamil Chollampatt and Hwee Tou Ng. 2017. Connecting the Dots: Towards Human-Level Grammatical Error Correction. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 327–333, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Connecting the Dots: Towards Human-Level Grammatical Error Correction (Chollampatt & Ng, BEA 2017)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/W17-5037.pdf
- Data
- CoNLL-2014 Shared Task: Grammatical Error Correction, JFLEG