比赛时刻匹配：迈向自动生成足球比赛评论

MatchTime: Towards Automatic Soccer Game Commentary Generation

June 26, 2024

作者: Jiayuan Rao, Haoning Wu, Chang Liu, Yanfeng Wang, Weidi Xie

cs.AI

摘要

足球是一项全球受欢迎的运动，拥有庞大的观众群。在本文中，我们考虑构建一个自动足球比赛评论模型，以提升观众的观赏体验。总体而言，我们做出以下贡献：首先，观察到现有数据集中普遍存在的视频文本不对齐问题，我们手动为49场比赛注释时间戳，建立了一个更为健壮的足球比赛评论生成基准，命名为SN-Caption-test-align；其次，我们提出了一个多模态时间对齐流程，以自动纠正和过滤现有数据集，规模化地创建了一个更高质量的足球比赛评论数据集用于训练，标记为MatchTime；第三，基于我们精心筛选的数据集，我们训练了一个自动生成评论的模型，命名为MatchVoice。大量实验和消融研究已经证明了我们的对齐流程的有效性，以及在精心筛选的数据集上训练模型实现了评论生成的最新性能，展示了更好的对齐可以显著提升下游任务的性能。

English

Soccer is a globally popular sport with a vast audience, in this paper, we consider constructing an automatic soccer game commentary model to improve the audiences' viewing experience. In general, we make the following contributions: First, observing the prevalent video-text misalignment in existing datasets, we manually annotate timestamps for 49 matches, establishing a more robust benchmark for soccer game commentary generation, termed as SN-Caption-test-align; Second, we propose a multi-modal temporal alignment pipeline to automatically correct and filter the existing dataset at scale, creating a higher-quality soccer game commentary dataset for training, denoted as MatchTime; Third, based on our curated dataset, we train an automatic commentary generation model, named MatchVoice. Extensive experiments and ablation studies have demonstrated the effectiveness of our alignment pipeline, and training model on the curated datasets achieves state-of-the-art performance for commentary generation, showcasing that better alignment can lead to significant performance improvements in downstream tasks.