Detecting Deception in Political DebatesUsing Acoustic and Textual Features
Table 2: Logistic regression experiments.
['[BOLD] Model & Features', '[BOLD] MAE', '[BOLD] MMAE', '[BOLD] Acc', '[BOLD] F1', '[BOLD] MAR']
We can see that the best individual feature type is BERT, with a MAE of 0.76. It is nearly tied with i–vector, which has a MAE of 0.77, and it is also the best overall in terms of MMAE, F1, and MAR. At the third position with a MAE of 0.81 is TF.IDF n-grams, which performed best overall in terms of Accuracy. The remaining two feature types could not improve over a random baseline. Overall, both textual and acoustic features seem to be important, as we find BERT and i–vector as the top-2 based on four of our five evaluation measures. We can see that none of the combinations could improve over the top-2 systems: this is probably due to the small number of training examples, which made it hard for the combined models to handle the increased number of parameters.