MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Document

Zheng Chu, Zekun Wang, Jiafeng Liang, Ming Liu, Bing Qin

Abstract
The facts and time in the document are intricately intertwined, making temporal reasoning over documents challenging. Previous work models time implicitly, making it difficult to handle such complex relationships. To address this issue, we propose MTGER, a novel Multi-view Temporal Graph Enhanced Reasoning framework for temporal reasoning over time-involved documents. Concretely, MTGER explicitly models the temporal relationships among facts by multi-view temporal graphs. On the one hand, the heterogeneous temporal graphs explicitly model the temporal and discourse relationships among facts; on the other hand, the multi-view mechanism captures both time-focused and fact-focused information, allowing the two views to complement each other through adaptive fusion. To further improve the implicit reasoning capability of the model, we design a self-supervised time-comparing objective. Extensive experimental results demonstrate the effectiveness of our method on the TimeQA and SituatedQA datasets. Furthermore, MTGER gives more consistent answers under question perturbations.
Anthology ID:
2023.findings-emnlp.1016
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
15218–15233
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.1016
DOI:
10.18653/v1/2023.findings-emnlp.1016
Bibkey:
Cite (ACL):
Zheng Chu, Zekun Wang, Jiafeng Liang, Ming Liu, and Bing Qin. 2023. MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Document. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15218–15233, Singapore. Association for Computational Linguistics.
Cite (Informal):
MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Document (Chu et al., Findings 2023)
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https://preview.aclanthology.org/naacl24-info/2023.findings-emnlp.1016.pdf
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