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        <namePart type="given">Jiayuan</namePart>
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        <namePart type="given">Haoning</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T MatchTime: Towards Automatic Soccer Game Commentary Generation
%A Rao, Jiayuan
%A Wu, Haoning
%A Liu, Chang
%A Wang, Yanfeng
%A Xie, Weidi
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F rao-etal-2024-matchtime
%X 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.
%U https://aclanthology.org/2024.emnlp-main.99
%P 1671-1685

Markdown (Informal)

[MatchTime: Towards Automatic Soccer Game Commentary Generation](https://aclanthology.org/2024.emnlp-main.99) (Rao et al., EMNLP 2024)

• MatchTime: Towards Automatic Soccer Game Commentary Generation (Rao et al., EMNLP 2024)

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.