GECToR – Grammatical Error Correction: Tag, Not Rewrite
Kostiantyn Omelianchuk, Vitaliy Atrasevych, Artem Chernodub, Oleksandr Skurzhanskyi
Abstract
In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder. Our system is pre-trained on synthetic data and then fine-tuned in two stages: first on errorful corpora, and second on a combination of errorful and error-free parallel corpora. We design custom token-level transformations to map input tokens to target corrections. Our best single-model/ensemble GEC tagger achieves an F_0.5 of 65.3/66.5 on CONLL-2014 (test) and F_0.5 of 72.4/73.6 on BEA-2019 (test). Its inference speed is up to 10 times as fast as a Transformer-based seq2seq GEC system.- Anthology ID:
- 2020.bea-1.16
- Volume:
- Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- July
- Year:
- 2020
- Address:
- Seattle, WA, USA → Online
- Venues:
- ACL | BEA | WS
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 163–170
- URL:
- https://www.aclweb.org/anthology/2020.bea-1.16
- DOI:
- PDF:
- https://www.aclweb.org/anthology/2020.bea-1.16.pdf
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