@inproceedings{arora-2020-inltk,
    title = "i{NLTK}: Natural Language Toolkit for Indic Languages",
    author = "Arora, Gaurav",
    editor = "Park, Eunjeong L.  and
      Hagiwara, Masato  and
      Milajevs, Dmitrijs  and
      Liu, Nelson F.  and
      Chauhan, Geeticka  and
      Tan, Liling",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.nlposs-1.10/",
    doi = "10.18653/v1/2020.nlposs-1.10",
    pages = "66--71",
    abstract = "We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Data Augmentation, Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages. By using pre-trained models from iNLTK for text classification on publicly available datasets, we significantly outperform previously reported results. On these datasets, we also show that by using pre-trained models and data augmentation from iNLTK, we can achieve more than 95{\%} of the previous best performance by using less than 10{\%} of the training data. iNLTK is already being widely used by the community and has 40,000+ downloads, 600+ stars and 100+ forks on GitHub. The library is available at \url{https://github.com/goru001/inltk}."
}