  Recent years have witnessed an increasing amount of dialogue/conversation on
the web especially on social media. That inspires the development of
dialogue-based retrieval, in which retrieving videos based on dialogue is of
increasing interest for recommendation systems. Different from other video
retrieval tasks, dialogue-to-video retrieval uses structured queries in the
form of user-generated dialogue as the search descriptor. We present a novel
dialogue-to-video retrieval system, incorporating structured conversational
information. Experiments conducted on the AVSD dataset show that our proposed
approach using plain-text queries improves over the previous counterpart model
by 15.8% on R@1. Furthermore, our approach using dialogue as a query, improves
retrieval performance by 4.2%, 6.2%, 8.6% on R@1, R@5 and R@10 and outperforms
the state-of-the-art model by 0.7%, 3.6% and 6.0% on R@1, R@5 and R@10
respectively.

Information Retrieval;Artificial Intelligence