@inproceedings{pawar-etal-2021-weakly,
    title = "Weakly Supervised Extraction of Tasks from Text",
    author = "Pawar, Sachin  and
      Palshikar, Girish  and
      Sinha Banerjee, Anindita",
    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/ingest-emnlp/2021.icon-main.71/",
    pages = "583--592",
    abstract = "In this paper, we propose a novel problem of automatic extraction of tasks from text. A task is a well-defined knowledge-based volitional action. We describe various characteristics of tasks as well as compare and contrast them with events. We propose two techniques for task extraction {--} i) using linguistic patterns and ii) using a BERT-based weakly supervised neural model. We evaluate our techniques with other competent baselines on 4 datasets from different domains. Overall, the BERT-based weakly supervised neural model generalizes better across multiple domains as compared to the purely linguistic patterns based approach."
}