Abstract
We propose to leverage news discourse profiling to model document-level temporal structures for building temporal dependency graphs. Our key observation is that the functional roles of sentences used for profiling news discourse signify different time frames relevant to a news story and can, therefore, help to recover the global temporal structure of a document. Our analyses and experiments with the widely used knowledge distillation technique show that discourse profiling effectively identifies distant inter-sentence event and (or) time expression pairs that are temporally related and otherwise difficult to locate.- Anthology ID:
- 2022.aacl-short.44
- Volume:
- Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2022
- Address:
- Online only
- Editors:
- Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
- Venues:
- AACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 357–365
- Language:
- URL:
- https://aclanthology.org/2022.aacl-short.44
- DOI:
- Cite (ACL):
- Prafulla Kumar Choubey and Ruihong Huang. 2022. Modeling Document-level Temporal Structures for Building Temporal Dependency Graphs. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 357–365, Online only. Association for Computational Linguistics.
- Cite (Informal):
- Modeling Document-level Temporal Structures for Building Temporal Dependency Graphs (Choubey & Huang, AACL-IJCNLP 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.aacl-short.44.pdf