Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering

Xin Su, Phillip Howard, Nagib Hakim, Steven Bethard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Answering time-sensitive questions from long documents requires temporal reasoning over the times in questions and documents. An important open question is whether large language models can perform such reasoning solely using a provided text document, or whether they can benefit from additional temporal information extracted using other systems. We address this research question by applying existing temporal information extraction systems to construct temporal graphs of events, times, and temporal relations in questions and documents. We then investigate different approaches for fusing these graphs into Transformer models. Experimental results show that our proposed approach for fusing temporal graphs into input text substantially enhances the temporal reasoning capabilities of Transformer models with or without fine-tuning. Additionally, our proposed method outperforms various graph convolution-based approaches and establishes a new state-of-the-art performance on SituatedQA and three splits of TimeQA.

Original languageEnglish (US)
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationEMNLP 2023
PublisherAssociation for Computational Linguistics (ACL)
Pages948-966
Number of pages19
ISBN (Electronic)9798891760615
StatePublished - 2023
Event2023 Findings of the Association for Computational Linguistics: EMNLP 2023 - Singapore, Singapore
Duration: Dec 6 2023Dec 10 2023

Publication series

NameFindings of the Association for Computational Linguistics: EMNLP 2023

Conference

Conference2023 Findings of the Association for Computational Linguistics: EMNLP 2023
Country/TerritorySingapore
CitySingapore
Period12/6/2312/10/23

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems
  • Language and Linguistics
  • Linguistics and Language

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