  Inspired by recent trends in vision and language learning, we explore
applications of attention mechanisms for visio-lingual fusion within an
application to story-based video understanding. Like other video-based QA
tasks, video story understanding requires agents to grasp complex temporal
dependencies. However, as it focuses on the narrative aspect of video it also
requires understanding of the interactions between different characters, as
well as their actions and their motivations. We propose a novel co-attentional
transformer model to better capture long-term dependencies seen in visual
stories such as dramas and measure its performance on the video question
answering task. We evaluate our approach on the recently introduced DramaQA
dataset which features character-centered video story understanding questions.
Our model outperforms the baseline model by 8 percentage points overall, at
least 4.95 and up to 12.8 percentage points on all difficulty levels and
manages to beat the winner of the DramaQA challenge.
