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“Does it Matter When I Think You Are Lying?” Improving Deception Detection by Integrating Interlocutor's Judgements in Conversations
Abstract
It is well known that human is not good at deception detection because of a natural inclination of truth-bias. However, during a conversation, when an interlocutor (interrogator) is being asked explicitly to assess whether his/her interacting partner (deceiver) is lying, this perceptual judgment depends highly on how the interrogator interprets the context of the conversation. While the deceptive behaviors can be difficult to model due to their heterogeneous manifestation, we hypothesize that this contextual information, i.e., whether the interlocutor trusts or distrusts what his/her partner is saying, provides an important condition in which the deceiver's deceptive behaviors are more consistently distinct. In this work, we propose a Judgmental-Enhanced Automatic Deception Detection Network (JEADDN) that explicitly considers interrogator's perceived truths-deceptions with three types of speechlanguage features (acoustic-prosodic, linguistic, and conversational temporal dynamics features) extracted during a conversation. We evaluate our framework on a large Mandarin Chinese Deception Dialog Database. The results show that the method significantly outperforms the current state-of-the-art approach without conditioning on the judgements of interrogators on this database. We further demonstrate that the behaviors of interrogators are important in detecting deception when the interrogators distrust the deceivers. Finally, with the late fusion of audio, text, and turntaking dynamics (TTD) features, we obtain promising results of 87.27% and 94.18% accuracy under the conditions that the interrogators trust and distrust the deceivers in deception detection which improves 7.27% and 13.57% than the model without considering the judgements of interlocutor respectively.
Figures
The illustration of Questioning-Answering (QA) pair turns. We only used complete QA pair turns and excluded some questioning turns if we cannot find the corresponding answering turns.
The illustration of Questioning-Answering (QA) pair turns. We only used complete QA pair turns and excluded some questioning turns if we cannot find the corresponding answering turns.
Keywords
deception detection | human judgements | dyadic conversations
Authors
Publication Date
2021/08/07
Conference
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)
DOI
10.18653/v1/2021.findings-acl.162