@inproceedings{mathur-etal-2022-doctime,
    title = "{D}oc{T}ime: A Document-level Temporal Dependency Graph Parser",
    author = "Mathur, Puneet  and
      Morariu, Vlad  and
      Kaynig-Fittkau, Verena  and
      Gu, Jiuxiang  and
      Dernoncourt, Franck  and
      Tran, Quan  and
      Nenkova, Ani  and
      Manocha, Dinesh  and
      Jain, Rajiv",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.naacl-main.73/",
    doi = "10.18653/v1/2022.naacl-main.73",
    pages = "993--1009",
    abstract = "We introduce DocTime - a novel temporal dependency graph (TDG) parser that takes as input a text document and produces a temporal dependency graph. It outperforms previous BERT-based solutions by a relative 4-8{\%} on three datasets from modeling the problem as a graph network with path-prediction loss to incorporate longer range dependencies. This work also demonstrates how the TDG graph can be used to improve the downstream tasks of temporal questions answering and NLI by a relative 4-10{\%} with a new framework that incorporates the temporal dependency graph into the self-attention layer of Transformer models (Time-transformer). Finally, we develop and evaluate on a new temporal dependency graph dataset for the domain of contractual documents, which has not been previously explored in this setting."
}