Toward multimedia: A string pattern-based passage ranking model for video question answering

In this paper, we present a new string pattern matching-based passage ranking algorithm for extending traditional text-based QA toward videoQA. Users interact with our videoQA system through natural language questions, while our system returns passage fragments with corresponding video clips as answers. We collect 75.6 hours videos and 253 Chinese questions for evaluation. The experimental results showed that our method outperformed six top-performed ranking models. It is 10.16% better than the second best method (language model) in relatively MRR score and 6.12% in precision rate. Besides, we also show that the use of a trained Chinese word segmentation tool did decrease the overall videoQA performance where most ranking algorithms dropped at least 10% in relatively MRR, precision, and answer pattern recall rates.