HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews
Table 3: Results for Monomodal models
['Model', 'Text  [ITALIC] Precision', 'Text  [ITALIC] Recall', 'Text  [BOLD] F1', 'Audio  [ITALIC] Precision', 'Audio  [ITALIC] Recall', 'Audio  [BOLD] F1', 'Video  [ITALIC] Precision', 'Video  [ITALIC] Recall', 'Video  [BOLD] F1']
We can also see that HN_AVG is superior to the Bidirectional-GRU baselines for audio and text validating H2 for those two modalities. This suggests that sequentiality and hierarchy are adequate inductive biases for a job interview assessment machine learning algorithm. As for H3, HN_SATT did show better results than HN_AVG, for text and audio. In the end, our HireNet model surpasses HN_AVG and HN_SATT for each modality. Consequently, a fair amount of useful information is present in the contextual frame of an interview, and this information can be leveraged through our model, as it is stated in H4. Audio and text monomodal models display better performance than video models. Our attempts at fusing the multimodal information synthesized in the last layer of each HireNet model only slightly improved on the single modality models.