NLPashto: NLP Toolkit for Low-resource Pashto Language

Abstract

In recent years, natural language processing (NLP) has transformed numerous domains, becoming a vital area of research. However, the focus of NLP studies has predominantly centered on major languages like English, inadvertently neglecting low-resource languages like Pashto. Pashto, spoken by a population of over 50 million worldwide, remains largely unexplored in NLP research, lacking off-the-shelf resources and tools even for fundamental text-processing tasks. To bridge this gap, this study presents NLPashto, an open-source and publicly accessible NLP toolkit specifically designed for Pashto. The initial version of NLPashto introduces four state-of-the-art models for Spelling Correction, Word Segmentation, Part-of-Speech (POS) Tagging, and Offensive Language Detection. The toolkit also includes essential NLP resources like pre-trained static word embeddings, Word2Vec, fastText, and GloVe. Furthermore, we have pre-trained a monolingual language model for Pashto from scratch, using the Bidirectional Encoder Representations from Transformers (BERT) architecture. For the training and evaluation of all the models, we have developed several benchmark datasets and also included them in the toolkit. Experimental results demonstrate that the models exhibit satisfactory performance in their respective tasks. This study can be a significant milestone and will hopefully support and speed-up future research in the field of Pashto NLP.