Two Neural Models for Multilingual Grammatical Error Detection
Keywords:grammatical error detection, neural models, multilingual
AbstractThis 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.
Copyright (c) 2023 Phuong Le-Hong, The Quyen Ngo, Thi Minh Huyen Nguyen
This work is licensed under a Creative Commons Attribution 4.0 International License.