MatchTime: Towards Automatic Soccer Game Commentary Generation

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


Abstract
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.
Anthology ID:
2024.emnlp-main.99
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1671–1685
Language:
URL:
https://aclanthology.org/2024.emnlp-main.99
DOI:
10.18653/v1/2024.emnlp-main.99
Bibkey:
Cite (ACL):
Jiayuan Rao, Haoning Wu, Chang Liu, Yanfeng Wang, and Weidi Xie. 2024. MatchTime: Towards Automatic Soccer Game Commentary Generation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1671–1685, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
MatchTime: Towards Automatic Soccer Game Commentary Generation (Rao et al., EMNLP 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.emnlp-main.99.pdf