An Analysis Towards Dialogue-Based Deception Detection

When humans attempt to detect deception, they perform two actions: looking for telltale signs of deception, and asking questions to attempt to unveil a deceptive conversational partner. There has been significant prior work on automatic deception detection that attempts to learn signs of deception. On the other hand, we focus on the second action, envisioning a dialogue system that asks questions to attempt to catch a potential liar. In this paper, we describe the results of an initial analysis towards this goal, attempting to make clear which questions make the features of deception more salient. In order to do so, we collect a deceptive corpus in Japanese, our target language, perform an analysis of this corpus comparing with a similar English corpus, and perform an analysis of what kinds of questions result in a higher deception detection accuracy.