PLVCG: A Pretraining Based Model for Live Video Comment Generation

Live video comment generating task aims to automatically generate real-time viewer comments on videos like real viewers do. Like providing search suggestions by search engines, this task can help viewers find comments they want to post by providing generated comments. Previous works ignore the interactivity and diversity of comments and can only generate general and popular comments. In this paper, we incorporate post time of the comments to deal with the real-time related comment interactions. We also take the video type labels into consideration to handle the diversity of comments and generate more related and informative comments. To this end, we propose a pre-training based encoder-decoder joint model called PLVCG model. This model is composed of a bidirectional encoder to encode context comments and visual frames jointly as well as an auto-regressive decoder to generate real-time comments and classify the type of the video. We evaluate our model in a large-scale real-world live comment dataset. The experiment results present that our model outperforms the state-of-the-art on live video comment ranking and generating task significantly.