Large-scale multimodal movie dialogue corpus

Abstract

We present an outline of our newly created multimodal dialogue corpus that is constructed from public domain movies. Dialogues in movies are useful sources for analyzing human communication patterns. In addition, they can be used to train machine-learning-based dialogue processing systems. However, the movie files are processing intensive and they contain large portions of non-dialogue segments. Therefore, we created a corpus that contains only dialogue segments from movies. The corpus contains 165,368 dialogue segments taken from 1,722 movies. These dialogues are automatically segmented by using deep neural network-based voice activity detection with filtering rules. Our corpus can reduce the human workload and machine-processing effort required to analyze human dialogue behavior by using movies.