Diversified Co-Attention towards Informative Live Video Commenting
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We focus on the task of Automatic Live Video Commenting (ALVC), which aims to generate real-time video comments based on both video frames and other viewers' remarks. An intractable challenge in this task is the appropriate modeling of complex dependencies between video and textual inputs. Previous work in the ALVC task applies separate attention on these two input sources to obtain their representations. In this paper, we argue that the information of video and text should be modeled integrally. We propose a novel model equipped with a Diversified Co-Attention layer (DCA) and a Gated Attention Module (GAM). DCA allows interactions between video and text from diversified perspectives via metric learning, while GAM collects an informative context for comment generation. We further introduce a parameter orthogonalization technique to allieviate information redundancy in DCA. Experiment results show that our model outperforms previous approaches in the ALVC task and the traditional co-attention model, achieving state-of-the-art results.
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