Engaging video comments play an important role in video social media, as they are the carrier of feelings, thoughts, or humor of the audience. Preliminary works have made initial exploration for video comment generation by adopting caption-style encoder-decoder models. However, comment generation presents some unique challenges distinct from caption generation, which makes these methods somewhat less effective at generating engaging comments. In contrast to the objective and descriptive nature of captions, comments tend to be inherently subjective, making it hard to quantify and evaluate the engagement of comments. Furthermore, the scarcity of truly engaging comments brings difficulty to collecting enough high-quality training examples. In this paper, we propose ViCo with three novel designs to tackle the above challenges for generating engaging Video Comments. Firstly, to quantify the engagement of comments, we utilize the number of "likes" each comment receives as a proxy of human preference after an appropriate debiasing procedure. Secondly, to automatically evaluate the engagement of comments, we train a reward model to align its judgment to the above proxy. Our user studies indicate that this reward model effectively aligns with human judgments. Lastly, to alleviate the scarcity of high-quality comments, an initial generator is trained on readily available but noisy data to generate comments. Then the reward model is employed to offer feedback on the generated comments, thus optimizing the initial generator. To facilitate the research of video commenting, we collect a large video comment-dataset (ViCo-20k) with rich metadata from a popular video website. Experiments on ViCo-20k show that the comments generated by our ViCo model exhibit the best performance in terms of both quantitative and qualitative results, particularly when engagement is considered.

翻译：引人入胜的视频评论在视频社交媒体中扮演着重要角色，它们承载着观众的情感、思想或幽默。已有研究采用类似图像描述的编码器-解码器模型对视频评论生成进行了初步探索。然而，评论生成与描述生成存在独特挑战，导致现有方法在生成吸引性评论时效果欠佳。与客观描述性的标题不同，评论本质上具有主观性，这使得量化评估评论的吸引力变得困难。此外，真正吸引人的评论稀缺，给收集足够高质量训练样本带来难度。本文提出ViCo模型，通过三项创新设计应对上述挑战，以生成吸引人的视频评论。首先，为量化评论吸引力，我们采用去偏处理后每条评论获得的"点赞"数量作为人类偏好的代理指标。其次，为自动评估评论吸引力，我们训练奖励模型使其判断与该代理指标对齐。用户研究表明该奖励模型有效契合人类判断。最后，为缓解高质量评论稀缺问题，先利用易获取但含噪声数据训练初始生成器生成评论，再通过奖励模型对生成结果提供反馈，从而优化初始生成器。为促进视频评论研究，我们从主流视频网站收集了包含丰富元数据的海量视频评论数据集ViCo-20k。在ViCo-20k上的实验表明，我们的ViCo模型生成的评论在定量与定性评估中均表现最优，尤其在考虑吸引力指标时更为突出。