  We introduce the task of automatic live commenting. Live commenting, which is
also called `video barrage', is an emerging feature on online video sites that
allows real-time comments from viewers to fly across the screen like bullets or
roll at the right side of the screen. The live comments are a mixture of
opinions for the video and the chit chats with other comments. Automatic live
commenting requires AI agents to comprehend the videos and interact with human
viewers who also make the comments, so it is a good testbed of an AI agent's
ability of dealing with both dynamic vision and language. In this work, we
construct a large-scale live comment dataset with 2,361 videos and 895,929 live
comments. Then, we introduce two neural models to generate live comments based
on the visual and textual contexts, which achieve better performance than
previous neural baselines such as the sequence-to-sequence model. Finally, we
provide a retrieval-based evaluation protocol for automatic live commenting
where the model is asked to sort a set of candidate comments based on the
log-likelihood score, and evaluated on metrics such as mean-reciprocal-rank.
Putting it all together, we demonstrate the first `LiveBot'.
