improving persian named entity recognition through multi task learning

named entity recognition is a challenging task, specially for low resource languages, such as persian, due to the lack of massive gold data. as developing manually-annotated datasets is time consuming and expensive, we use a multitask learning (mtl) framework to exploit different datasets to enrich the extracted features and improve the accuracy of recognizing named entities in persian news articles. highly motivated auxiliary tasks are chosen to be included in a deep learning based structure. additionally, we investigate the effect of chosen datasets on performance of the model. our best model significantly outperformed the state of the art model by 𝟏.𝟗𝟓%, according to f1 score in the phrase level.