  Automatic deception detection is an important task that has gained momentum
in computational linguistics due to its potential applications. In this paper,
we propose a simple yet tough to beat multi-modal neural model for deception
detection. By combining features from different modalities such as video,
audio, and text along with Micro-Expression features, we show that detecting
deception in real life videos can be more accurate. Experimental results on a
dataset of real-life deception videos show that our model outperforms existing
techniques for deception detection with an accuracy of 96.14% and ROC-AUC of
0.9799.
