Discourse Information for Document-Level Temporal Dependency Parsing

TL;DR

We examine whether discourse information helps document-level temporal dependency parsing. Unexpectedly, simple sentence-position information — encoded with our novel sentence-position embedding — outperforms high-level discourse profiling features, perhaps because it does not rely on noisy model-generated inputs, and our system surpasses the previous state-of-the-art TDG parsers. We argue that discourse analysis should inform temporal information extraction more deeply than as just another input feature.

How to Cite

@inproceedings{niu-etal-2023-discourse,
  title = "Discourse Information for Document-Level Temporal Dependency Parsing",
  author = "Niu, Jingcheng and
   Ng, Victoria and
   Rees, Erin and
   De Montigny, Simon and
   Penn, Gerald",
  booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023)",
  month = jul,
  year = "2023",
  address = "Toronto, Canada",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2023.codi-1.10/",
  doi = "10.18653/v1/2023.codi-1.10",
  pages = "82--88"
}