NTNU-TRH system at the MultiGED-2023 Shared on Multilingual Grammatical Error Detection


  • Lars Bungum
  • Björn Gambäck
  • Arild Brandrud Næss




Grammatical Error Detection, Flair Embeddings, FlairNLP, BERT, Multitask Learning, MGED, GED


The paper presents a monolithic approach to grammatical error detection, which uses one model for all languages, in contrast to the individual approach, which creates separate models for each language. For both approaches, pre-trained embeddings are the only external knowledge sources. Two sets of embeddings (Flair and BERT) are compared as well as two approaches to the problem of multilingual rammar detection, building individual and monolithic systems for multilingual grammar error detection. The system submitted to the test phase of the MultiGED-2023 shared task ranked 5th of 6 systems. In the subsequent open phase, more experiments were conducted, improving results. These results show the individual models to perform better than the monolithic ones and BERT embeddings working better than Flair embeddings for the individual models, while the picture is more mixed for the monolithic models.