SoccerNarrate: Event-Grounded Streaming Soccer Commentary with Macro-Window Preference Alignment

摘要

Existing soccer commentary models are often designed for pre-segmented clips or localized events. When deployed on untrimmed full-match videos with sliding windows, they can produce delayed, repeated, or poorly synchronized commentary. Recent streaming video-language models make low-latency full-match narration feasible, but they often favor general real-time descriptions rather than professional, event-grounded soccer commentary. They may describe nearby actions while failing to mention key events such as goals, cards, substitutions, and offsides correctly and on time. In this paper, we introduce **SoccerNarrate**, a data, model, and evaluation framework that bridges low-latency streaming narration with event-grounded professional soccer commentary. First, we construct **SoccerNarrate-Data**, a large-scale time-anchored dataset with word-level ASR alignment, quality filtering, and entity calibration. Second, we perform **macro-window preference alignment(MWPA)**, which aligns the model with complete event-level commentary semantics by comparing causal multi-step rollouts from the same streaming prefix while keeping second-level inference unchanged. We further use event-mismatched human commentaries as counterfactual negatives to emphasize event semantics over commentary style. Third, we introduce **SoccerNarrate-Eval**, an event-centric benchmark based on temporally constrained event entailment. Full-match experiments show that SoccerNarrate improves event coverage and event-aligned precision over strong offline and streaming baselines. Data and models will be released.