  With increasing information from social media, there are more and more videos
available. Therefore, the ability to reason on a video is important and
deserves to be discussed. TheDialog System Technology Challenge (DSTC7)
(Yoshino et al. 2018) proposed an Audio Visual Scene-aware Dialog (AVSD) task,
which contains five modalities including video, dialogue history, summary, and
caption, as a scene-aware environment. In this paper, we propose the
entropy-enhanced dynamic memory network (DMN) to effectively model video
modality. The attention-based GRU in the proposed model can improve the model's
ability to comprehend and memorize sequential information. The entropy
mechanism can control the attention distribution higher, so each to-be-answered
question can focus more specifically on a small set of video segments. After
the entropy-enhanced DMN secures the video context, we apply an attention model
that in-corporates summary and caption to generate an accurate answer given the
question about the video. In the official evaluation, our system can achieve
improved performance against the released baseline model for both subjective
and objective evaluation metrics.
