Command Palette
Search for a command to run...
Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models
Kostiantyn Omelianchuk Andrii Liubonko Oleksandr Skurzhanskyi Artem Chernodub Oleksandr Korniienko Igor Samokhin
Abstract
In this paper, we carry out experimental research on Grammatical Error Correction, delving into the nuances of single-model systems, comparing the efficiency of ensembling and ranking methods, and exploring the application of large language models to GEC as single-model systems, as parts of ensembles, and as ranking methods. We set new state-of-the-art performance with F_0.5 scores of 72.8 on CoNLL-2014-test and 81.4 on BEA-test, respectively. To support further advancements in GEC and ensure the reproducibility of our research, we make our code, trained models, and systems' outputs publicly available.
Code Repositories
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| grammatical-error-correction-on-bea-2019-test | Majority-voting ensemble on best 7 models | F0.5: 81.4 |
| grammatical-error-correction-on-conll-2014 | Majority-voting ensemble on best 7 models | F0.5: 71.8 Precision: 83.7 Recall: 45.7 |
| grammatical-error-correction-on-conll-2014 | Ensembles of best 7 models + GRECO + GTP-rerank | F0.5: 72.8 Precision: 83.9 Recall: 47.5 |
Build AI with AI
From idea to launch — accelerate your AI development with free AI co-coding, out-of-the-box environment and best price of GPUs.