# Learning to combine Grammatical Error Corrections

## TL;DR

This paper introduces an automatic method to combine multiple grammatical error correction systems, enhancing accuracy and outperforming existing ensemble and individual models, with promising results using BERT and a custom spellchecker.

## Contribution

It presents a novel automatic system combination approach for GEC that optimizes F score and improves over standalone and ensemble models, including BERT-based methods.

## Key findings

- Consistent improvement over best standalone systems
- Outperforms average ensembling of RNN models
- Achieves highest reported score in BEA 2019 shared task

## Abstract

The field of Grammatical Error Correction (GEC) has produced various systems to deal with focused phenomena or general text editing. We propose an automatic way to combine black-box systems. Our method automatically detects the strength of a system or the combination of several systems per error type, improving precision and recall while optimizing $F$ score directly. We show consistent improvement over the best standalone system in all the configurations tested. This approach also outperforms average ensembling of different RNN models with random initializations. In addition, we analyze the use of BERT for GEC - reporting promising results on this end. We also present a spellchecker created for this task which outperforms standard spellcheckers tested on the task of spellchecking. This paper describes a system submission to Building Educational Applications 2019 Shared Task: Grammatical Error Correction. Combining the output of top BEA 2019 shared task systems using our approach, currently holds the highest reported score in the open phase of the BEA 2019 shared task, improving F0.5 by 3.7 points over the best result reported.