MatchTime: Towards Automatic Soccer Game Commentary Generation
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.
667d26e002ffd4993e4eeaea
Jiayuan Rao
claimed_verified
2024-06-27T10:21:43.714Z
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/avatars/686cd85cf8d3d8f0beb0737811144294.svg
Jiayuan Rao 饶珈源
user
Homie0609
667d26e002ffd4993e4eeaeb
Haoning Wu
claimed_verified
2024-06-27T09:01:55.266Z
632c7a0d1d303f5f9acf01b8
https://cdn-avatars.huggingface.co/v1/production/uploads/632c7a0d1d303f5f9acf01b8/T010IFuCp6UaOeIyWhbCk.jpeg
Haoning Wu
user
haoningwu
667d26e002ffd4993e4eeaec
Chang Liu
667d26e002ffd4993e4eeaed
Yanfeng Wang
667d26e002ffd4993e4eeaee
Weidi Xie
admin_assigned
2024-06-27T18:19:17.935Z
63f1841f2f7c0152e8748ec4
/avatars/5398658c7d0cf556531a625a4ca5d18a.svg
Xie
user
Weidi
MatchTime: Towards Automatic Soccer Game Commentary Generation
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.
A soccer game commentary generation model, MatchVoice, is developed using a new dataset and alignment pipeline to improve video-text synchronization and performance.
multi-modal temporal alignment SN-Caption-test-align MatchTime MatchVoice commentary generation