Grammatical error annotation tool for Hindi

Abstract

In recent years, researchers have started putting in extensive efforts for Natural Language Pro- cessing (NLP) in the context of Indic Languages like Hindi. Tasks like Spell and Grammar Correction, which have been thoroughly studied for languages like English, have gained mo- mentum. However, with limited data and tools available for Hindi, performing Grammatical Error Correction (GEC) for Hindi is a challenge. We propose InHerrant, a Grammatical ERRor ANnotation Toolkit for the Indic language Hindi. This tool is built to automatically extract edits from a parallel corpus of incorrect and correct sentences and classify them according to a new, dataset-agnostic, rule-based framework. InHerrant provides Hindi GEC researchers with a standardised metric for evaluation and reduces annotator workload and can classify edits based on their error-types at different levels of granularity. We also try to improve upon the models developed for Hindi GEC. We create an artificial dataset by introducing errors in a corpus of Hindi Wikipedia and train multiple state-of-the-art models developed for English for GEC in Hindi. We achieve state of the art results for Hindi GEC, surpassing the existing state of the art by 14.65 per cent in terms of F0.5 score.