  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.
