  Movie question answering, or MovieQA is a multimedia related task wherein one
is provided with a video, the subtitle information, a question and candidate
answers for it. The task is to predict the correct answer for the question
using the components of the multimedia - namely video/images, audio and text.
Traditionally, MovieQA is done using the image and text component of the
multimedia. In this paper, we propose a novel network with triple-attention
architecture for the inclusion of audio in the Movie QA task. This architecture
is fashioned after a traditional dual attention network focused only on video
and text. Experiments show that the inclusion of audio using the
triple-attention network results provides complementary information for Movie
QA task which is not captured by visual or textual component in the data.
Experiments with a wide range of audio features show that using such a network
can indeed improve MovieQA performance by about 7% relative to just using only
visual features.
