让视频问答具备跨段落推理能力，生成需要多步思考的问题。
VideoChain: A Transformer-Based Framework for Multi-hop Video Question Generation
- 基于改进的BART架构，融合视觉与文本信息建模多段视频依赖。
- 在TVQA+数据集上构建60万条多跳问答对，支持大规模训练与评估。
- 生成问题兼具语义连贯性与推理深度，适合研究视频理解与认知建模。
多跳问题生成（QG）能有效评估推理能力，但目前仅限于文本；视频问答（VideoQG）仍局限于单段视频的零跳问题。为此，我们提出VideoChain，一种新型的多跳视频问答生成框架，旨在生成需跨多个时间分离视频段落进行推理的问题。VideoChain采用模块化架构，基于改进的BART骨干网络并引入视频嵌入，捕捉文本与视觉间的依赖关系。利用TVQA+数据集，我们自动构建了大规模的MVQ-60数据集，通过合并零跳问答对实现可扩展性和多样性。评估结果显示，VideoChain在标准生成指标上表现优异：ROUGE-L（0.6454）、ROUGE-1（0.6854）、BLEU-1（0.6711）、BERTScore-F1（0.7967）和语义相似度（0.8110）。这些结果表明模型具备生成连贯、上下文准确且富含推理需求问题的能力。
原文摘要 · Abstract (English)
Multi-hop Question Generation (QG) effectively evaluates reasoning but remains confined to text; Video Question Generation (VideoQG) is limited to zero-hop questions over single segments. To address this, we introduce VideoChain, a novel Multi-hop Video Question Generation (MVQG) framework designed to generate questions that require reasoning across multiple, temporally separated video segments. VideoChain features a modular architecture built on a modified BART backbone enhanced with video embeddings, capturing textual and visual dependencies. Using the TVQA+ dataset, we automatically construct the large-scale MVQ-60 dataset by merging zero-hop QA pairs, ensuring scalability and diversity. Evaluations show VideoChain's strong performance across standard generation metrics: ROUGE-L (0.6454), ROUGE-1 (0.6854), BLEU-1 (0.6711), BERTScore-F1 (0.7967), and semantic similarity (0.8110). These results highlight the model's ability to generate coherent, contextually grounded, and reasoning-intensive questions.
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