Multimodal Event Transformer for Image-guided Story Ending Generation

• DOI: 10.18653/v1/2023.eacl-main.249
• Publication Date: 2023-09-09T20:54:31Z
• Authors: Yucheng Zhou; Guodong Long
• Keywords: FOS: Computer and information sciences; 03 medical and health sciences; Computer Science - Computation and Language; 0302 clinical medicine; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; Computation and Language (cs.CL)

Image-guided story ending generation (IgSEG) is to generate a story ending based on given story plots and ending image. Existing methods focus on cross-modal feature fusion but overlook reasoning and mining implicit information from story plots and ending image. To tackle this drawback, we propose a multimodal event transformer, an event-based reasoning framework for IgSEG. Specifically, we construct visual and semantic event graphs from story plots and ending image, and leverage event-based reasoning to reason and mine implicit information in a single modality. Next, we connect visual and semantic event graphs and utilize cross-modal fusion to integrate different-modality features. In addition, we propose a multimodal injector to adaptive pass essential information to decoder. Besides, we present an incoherence detection to enhance the understanding context of a story plot and the robustness of graph modeling for our model. Experimental results show that our method achieves state-of-the-art performance for the image-guided story ending generation.<br/>EACL 2023<br/>