Detecting Deception in Political DebatesUsing Acoustic and Textual Features
Table 3: Neural network experiments.
['[BOLD] Model & Features', '[BOLD] MAE', '[BOLD] MMAE', '[BOLD] Acc', '[BOLD] F1', '[BOLD] MAR']
The top half of the table shows the results when only one feature type is given as an input and the rest are set to zero. As in the logistic regression experiments, we see i–vector at the second-best position. However, this time the best feature type is LIWC + Speaker rather than BERT, which can be due to some information being lost when passing the BERT representation via two hidden layers as opposed to using it directly. We can see that this yields sizable improvements over using any individual feature type, which is in contrast to our combination experiments for the logistic regression experiments above. The following lines show ablation experiments where we leave one feature type out. Interestingly, even though the TF.IDF-weighted word n-gram features perform worse when used in isolation, they cause the largest drop for almost all evaluation measures, which suggests that they add some information that is complementary to what the remaining features already provide. The following two feature types whose removal causes the highest drop in performance are LIWC + Speakers and i–vector, which should not be surprising as these are the two best individual feature types from the top of the table.