Kostiantyn Omelianchuk


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GECToR – Grammatical Error Correction: Tag, Not Rewrite
Kostiantyn Omelianchuk | Vitaliy Atrasevych | Artem Chernodub | Oleksandr Skurzhanskyi
Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

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.


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How do you correct run-on sentences it’s not as easy as it seems
Junchao Zheng | Courtney Napoles | Joel Tetreault | Kostiantyn Omelianchuk
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text

Run-on sentences are common grammatical mistakes but little research has tackled this problem to date. This work introduces two machine learning models to correct run-on sentences that outperform leading methods for related tasks, punctuation restoration and whole-sentence grammatical error correction. Due to the limited annotated data for this error, we experiment with artificially generating training data from clean newswire text. Our findings suggest artificial training data is viable for this task. We discuss implications for correcting run-ons and other types of mistakes that have low coverage in error-annotated corpora.