Kostiantyn Omelianchuk


pdf bib
Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction
Maksym Tarnavskyi | Artem Chernodub | Kostiantyn Omelianchuk
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we investigate improvements to the GEC sequence tagging architecture with a focus on ensembling of recent cutting-edge Transformer-based encoders in Large configurations. We encourage ensembling models by majority votes on span-level edits because this approach is tolerant to the model architecture and vocabulary size. Our best ensemble achieves a new SOTA result with an F0.5 score of 76.05 on BEA-2019 (test), even without pre-training on synthetic datasets. In addition, we perform knowledge distillation with a trained ensemble to generate new synthetic training datasets, “Troy-Blogs” and “Troy-1BW”. Our best single sequence tagging model that is pretrained on the generated Troy- datasets in combination with the publicly available synthetic PIE dataset achieves a near-SOTA result with an F0.5 score of 73.21 on BEA-2019 (test). The code, datasets, and trained models are publicly available.


pdf bib
Text Simplification by Tagging
Kostiantyn Omelianchuk | Vipul Raheja | Oleksandr Skurzhanskyi
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications

Edit-based approaches have recently shown promising results on multiple monolingual sequence transduction tasks. In contrast to conventional sequence-to-sequence (Seq2Seq) models, which learn to generate text from scratch as they are trained on parallel corpora, these methods have proven to be much more effective since they are able to learn to make fast and accurate transformations while leveraging powerful pre-trained language models. Inspired by these ideas, we present TST, a simple and efficient Text Simplification system based on sequence Tagging, leveraging pre-trained Transformer-based encoders. Our system makes simplistic data augmentations and tweaks in training and inference on a pre-existing system, which makes it less reliant on large amounts of parallel training data, provides more control over the outputs and enables faster inference speeds. Our best model achieves near state-of-the-art performance on benchmark test datasets for the task. Since it is fully non-autoregressive, it achieves faster inference speeds by over 11 times than the current state-of-the-art text simplification system.


pdf bib
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.


pdf bib
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.