@inproceedings{pandya-etal-2021-cascading,
    title = "Cascading Adaptors to Leverage {E}nglish Data to Improve Performance of Question Answering for Low-Resource Languages",
    author = "Pandya, Hariom  and
      Ardeshna, Bhavik  and
      Bhatt, Brijesh",
    editor = "Bandyopadhyay, Sivaji  and
      Devi, Sobha Lalitha  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 18th International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2021",
    address = "National Institute of Technology Silchar, Silchar, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.icon-main.66/",
    pages = "544--549",
    abstract = "Transformer based architectures have shown notable results on many down streaming tasks including question answering. The availability of data, on the other hand, impedes obtaining legitimate performance for low-resource languages. In this paper, we investigate the applicability of pre-trained multilingual models to improve the performance of question answering in low-resource languages. We tested four combinations of language and task adapters using multilingual transformer architectures on seven languages similar to MLQA dataset. Additionally, we have also proposed zero-shot transfer learning of low-resource question answering using language and task adapters. We observed that stacking the language and the task adapters improves the multilingual transformer models' performance significantly for low-resource languages. Our code and trained models are available at: \url{https://github.com/CALEDIPQALL/}"
}