  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.
