Image Enhanced Event Detection in News Articles


  • Meihan Tong Tsinghua University
  • Shuai Wang Tsinghua University
  • Yixin Cao National university of Singapore
  • Bin Xu Tsinghua University
  • Juanzi Li Tsinghua University
  • Lei Hou Tsinghua University
  • Tat-Seng Chua National university of Singapore



Event detection is a crucial and challenging sub-task of event extraction, which suffers from a severe ambiguity issue of trigger words. Existing works mainly focus on using textual context information, while there naturally exist many images accompanied by news articles that are yet to be explored. We believe that images not only reflect the core events of the text, but are also helpful for the disambiguation of trigger words. In this paper, we first contribute an image dataset supplement to ED benchmarks (i.e., ACE2005) for training and evaluation. We then propose a novel Dual Recurrent Multimodal Model, DRMM, to conduct deep interactions between images and sentences for modality features aggregation. DRMM utilizes pre-trained BERT and ResNet to encode sentences and images, and employs an alternating dual attention to select informative features for mutual enhancements. Our superior performance compared to six state-of-art baselines as well as further ablation studies demonstrate the significance of image modality and effectiveness of the proposed architecture. The code and image dataset are avaliable at




How to Cite

Tong, M., Wang, S., Cao, Y., Xu, B., Li, J., Hou, L., & Chua, T.-S. (2020). Image Enhanced Event Detection in News Articles. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 9040-9047.



AAAI Technical Track: Natural Language Processing