  To understand movies, humans constantly reason over the dialogues and actions
shown in specific scenes and relate them to the overall storyline already seen.
Inspired by this behaviour, we design ROLL, a model for knowledge-based video
story question answering that leverages three crucial aspects of movie
understanding: dialog comprehension, scene reasoning, and storyline recalling.
In ROLL, each of these tasks is in charge of extracting rich and diverse
information by 1) processing scene dialogues, 2) generating unsupervised video
scene descriptions, and 3) obtaining external knowledge in a weakly supervised
fashion. To answer a given question correctly, the information generated by
each inspired-cognitive task is encoded via Transformers and fused through a
modality weighting mechanism, which balances the information from the different
sources. Exhaustive evaluation demonstrates the effectiveness of our approach,
which yields a new state-of-the-art on two challenging video question answering
datasets: KnowIT VQA and TVQA+.
