@inproceedings{patra-etal-2020-schedule,
    title = "To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints",
    author = "Patra, Barun  and
      Fufa, Chala  and
      Bhattacharya, Pamela  and
      Lee, Charles",
    editor = "Webber, Bonnie  and
      Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.emnlp-main.678/",
    doi = "10.18653/v1/2020.emnlp-main.678",
    pages = "8445--8455",
    abstract = "State of the art research for date-time entity extraction from text is task agnostic. Consequently, while the methods proposed in literature perform well for generic date-time extraction from texts, they don{'}t fare as well on task specific date-time entity extraction where only a subset of the date-time entities present in the text are pertinent to solving the task. Furthermore, some tasks require identifying negation constraints associated with the date-time entities to correctly reason over time. We showcase a novel model for extracting task-specific date-time entities along with their negation constraints. We show the efficacy of our method on the task of date-time understanding in the context of scheduling meetings for an email-based digital AI scheduling assistant. Our method achieves an absolute gain of 19{\%} f-score points compared to baseline methods in detecting the date-time entities relevant to scheduling meetings and a 4{\%} improvement over baseline methods for detecting negation constraints over date-time entities."
}