MatchTime：迈向自动足球比赛解说生成
MatchTime: Towards Automatic Soccer Game Commentary Generation
AI总结：
本文针对现有数据集视频与文本不对齐问题，构建了自动足球比赛解说模型，提出多模态时间对齐流水线校正数据集，并基于精选数据集训练解说生成模型，实现最先进性能。
摘要：
足球是一项拥有庞大观众的全球流行运动，本文考虑构建一个自动足球比赛解说模型以改善观众的观赛体验。总体而言，我们做出了以下贡献：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场比赛标注了时间戳，为足球比赛解说生成建立了一个更稳健的基准，称为SN-Caption-test-align；其次，我们提出一个多模态时间对齐流水线，以大规模自动校正和过滤现有数据集，创建了一个更高质量的用于训练的足球比赛解说数据集，记为MatchTime；第三，基于我们精选的数据集，我们训练了一个自动解说生成模型，名为MatchVoice。大量实验和消融研究证明了我们对齐流水线的有效性，并且在精选数据集上训练的模型在解说生成方面达到了最先进的性能，这表明更好的对齐能为下游任务带来显著的性能提升。
英文摘要：
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 dataset achieves state-of-the-art performance for commentary generation, showcasing that better alignment can lead to significant performance improvements in downstream tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
