Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction
Maksym Tarnavskyi, Artem Chernodub, Kostiantyn Omelianchuk
Abstract
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 F 0.5 score of 76.05 on BEA-2019 (test), even without pretraining 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 1 result with an F 0.5 score of 73.21 on BEA-2019 (test). The code, datasets, and trained models are publicly available. 2 * This research was performed during Maksym Tarnavskyi's work on Ms.Sc. thesis