Modeling Document-level Temporal Structures for Building Temporal Dependency Graphs

Prafulla Kumar Choubey, Ruihong Huang


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
Venues:
AACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
357–365
Language:
URL:
https://aclanthology.org/2022.aacl-short.44
DOI:
Bibkey:
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)
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PDF:
https://aclanthology.org/2022.aacl-short.44.pdf