MatchTime: Towards Automatic Soccer Game Commentary Generation (2406.18530v2)

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 dataset achieves state-of-the-art performance for commentary generation, showcasing that better alignment can lead to significant performance improvements in downstream tasks.

• The paper presents a multi-modal pipeline that corrects video-text misalignments in soccer videos to build a robust dataset.
• It integrates WhisperX, LLaMA-3, and the MatchVoice model to generate contextually rich and professional commentary.
• The approach achieves state-of-the-art performance on BLEU, METEOR, ROUGE-L, and GPT-score metrics, enhancing real-time sports broadcasting.

Insights into "MatchTime: Towards Automatic Soccer Game Commentary Generation"

The paper "MatchTime: Towards Automatic Soccer Game Commentary Generation" presents an advanced approach to automating soccer commentary using AI, addressing a specific challenge within the broader domain of sports video understanding. This research explores the alignment between video content and textual commentary, proposing significant improvements over existing datasets, and introducing a novel model, MatchVoice, for generating professional soccer game commentary.

The paper identifies critical issues in existing datasets, particularly the prevalent misalignment between visual content and textual commentaries in soccer videos. Recognizing that such misalignments undermine the efficacy of AI-generated commentary, the authors conduct a meticulous manual annotation of 49 soccer matches, curating a new benchmark, SN-Caption-test-align. This benchmark demonstrates an average absolute offset of 16.63 seconds in existing data, emphasizing the necessity for better temporal alignment.

The authors propose a multi-modal temporal alignment pipeline that corrects and filters existing datasets at scale. This pipeline uses WhisperX, an ASR system, to extract narration texts from video audio, which are then summarized into events using LLaMA-3, a LLM. Subsequently, the model refines alignment by employing a multi-modal temporal alignment model trained on the manually annotated data. This two-stage alignment process results in a high-quality dataset called MatchTime, substantially reducing the misalignment and serving as a robust foundation for training the MatchVoice model.

MatchVoice, the commentary generation model, leverages this curated dataset. It comprises a visual encoder, a temporal aggregator, and a LLM, integrating visual features into the LLM to generate commentary. When compared to other methodologies, MatchVoice achieves state-of-the-art performance across various metrics, including BLEU, METEOR, ROUGE-L, CIDEr, and GPT-score, underscoring its effectiveness in generating contextually relevant and professional commentary.

This paper's implications are noteworthy, with MatchTime and MatchVoice promising enhancements in both practical applications, such as real-time sports broadcasting, and theoretical advancements in AI models' ability to process and interpret dynamic video content. The alignment pipeline presents a novel approach that could be adapted to other domains requiring synchronization between visual data and textual descriptions.

For future directions, fine-tuning visual encoders and language decoders on soccer-specific data is suggested to further refine the commentary's accuracy. Moreover, incorporating player information and game context could enhance the granularity and relevance of generated commentaries, broadening the applications of AI in sports analytics and beyond.

In summary, the paper offers a significant contribution to the field of automatic sports commentary generation, demonstrating that addressing dataset misalignment can lead to substantial performance improvements. It sets a new standard for future research and applications within sports video understanding, inviting further innovations that leverage its methodologies.