ﻻ يوجد ملخص باللغة العربية
In this paper, we propose the task of live comment generation. Live comments are a new form of comments on videos, which can be regarded as a mixture of comments and chats. A high-quality live comment should be not only relevant to the video, but also interactive with other users. In this work, we first construct a new dataset for live comment generation. Then, we propose a novel end-to-end model to generate the human-like live comments by referring to the video and the other users comments. Finally, we evaluate our model on the constructed dataset. Experimental results show that our method can significantly outperform the baselines.
We analyze the claims that video recreations of shoulder surfing attacks offer a suitable alternative and a baseline, as compared to evaluation in a live setting. We recreated a subset of the factors of a prior video-simulation experiment conducted b
Automatic live commenting aims to provide real-time comments on videos for viewers. It encourages users engagement on online video sites, and is also a good benchmark for video-to-text generation. Recent work on this task adopts encoder-decoder model
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 diver
Call centers, in which human operators attend clients using textual chat, are very common in modern e-commerce. Training enough skilled operators who are able to provide good service is a challenge. We suggest an algorithm and a method to train and i
This paper proposes a method for generating bullet comments for live-streaming games based on highlights (i.e., the exciting parts of video clips) extracted from the game content and evaluate the effect of mental health promotion. Game live streaming