Grammatical Error Correction for Basque through a seq2seq neural architecture and synthetic examples

Abstract

Sequence-to-sequence neural architectures are the state of the art for addressing the task of correcting grammatical errors. However, large training datasets are required for this task. This paper studies the use of sequence-to-sequence neural models for the correction of grammatical errors in Basque. As there is no training data for this language, we have developed a rule-based method to generate grammatically incorrect sentences from a collection of correct sentences extracted from a corpus of 500,000 news in Basque. We have built different training datasets according to different strategies to combine the synthetic examples. From these datasets different models based on the Transformer architecture have been trained and evaluated according to accuracy, recall and F0.5 score. The results obtained with the best model reach 0.87 of F0.5 score.