title: MatchTime: Towards Automatic Soccer Game Commentary Generation
summary: 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.
paper_authors._id: 667d26e002ffd4993e4eeaea
paper_authors.name: Jiayuan Rao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-27T10:21:43.714Z
paper_authors._id: 6625ce8074ae2df4e3effa92
paper_authors.avatarUrl: /avatars/686cd85cf8d3d8f0beb0737811144294.svg
paper_authors.fullname: Jiayuan Rao 饶珈源
paper_authors.type: user
paper_authors.user: Homie0609
paper_authors._id: 667d26e002ffd4993e4eeaeb
paper_authors.name: Haoning Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-27T09:01:55.266Z
paper_authors._id: 632c7a0d1d303f5f9acf01b8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/632c7a0d1d303f5f9acf01b8/T010IFuCp6UaOeIyWhbCk.jpeg
paper_authors.fullname: Haoning Wu
paper_authors.type: user
paper_authors.user: haoningwu
paper_authors._id: 667d26e002ffd4993e4eeaec
paper_authors.name: Chang Liu
paper_authors._id: 667d26e002ffd4993e4eeaed
paper_authors.name: Yanfeng Wang
paper_authors._id: 667d26e002ffd4993e4eeaee
paper_authors.name: Weidi Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-27T18:19:17.935Z
paper_authors._id: 63f1841f2f7c0152e8748ec4
paper_authors.avatarUrl: /avatars/5398658c7d0cf556531a625a4ca5d18a.svg
paper_authors.fullname: Xie
paper_authors.type: user
paper_authors.user: Weidi
paper_title: MatchTime: Towards Automatic Soccer Game Commentary Generation
paper_summary: 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.
paper_ai_keywords: multi-modal temporal alignment commentary generation SN-Caption-test-align MatchTime MatchVoice