  We focus on the task of Automatic Live Video Commenting (ALVC), which aims to
generate real-time video comments with both video frames and other viewers'
comments as inputs. A major challenge in this task is how to properly leverage
the rich and diverse information carried by video and text. In this paper, we
aim to collect diversified information from video and text for informative
comment generation. To achieve this, we propose a Diversified Co-Attention
(DCA) model for this task. Our model builds bidirectional interactions between
video frames and surrounding comments from multiple perspectives via metric
learning, to collect a diversified and informative context for comment
generation. We also propose an effective parameter orthogonalization technique
to avoid excessive overlap of information learned from different perspectives.
Results show that our approach outperforms existing methods in the ALVC task,
achieving new state-of-the-art results.
