Two Neural Models for Multilingual Grammatical Error Detection

Abstract

This paper presents two neural models for multilingual grammatical error detection and their results in the MultiGED-2023 shared task. The first model uses a simple, purely supervised character-based approach. The second model uses a large language model which is pretrained on 100 different languages and fine-tuned on the provided datasets of the shared task. Despite simple approaches, the two systems achieved promising results. One system has the second best F-score; the other is in the top four of participating systems.