  New technologies drastically change recruitment techniques. Some research
projects aim at designing interactive systems that help candidates practice job
interviews. Other studies aim at the automatic detection of social signals
(e.g. smile, turn of speech, etc...) in videos of job interviews. These studies
are limited with respect to the number of interviews they process, but also by
the fact that they only analyze simulated job interviews (e.g. students
pretending to apply for a fake position). Asynchronous video interviewing tools
have become mature products on the human resources market, and thus, a popular
step in the recruitment process. As part of a project to help recruiters, we
collected a corpus of more than 7000 candidates having asynchronous video job
interviews for real positions and recording videos of themselves answering a
set of questions. We propose a new hierarchical attention model called HireNet
that aims at predicting the hirability of the candidates as evaluated by
recruiters. In HireNet, an interview is considered as a sequence of questions
and answers containing salient socials signals. Two contextual sources of
information are modeled in HireNet: the words contained in the question and in
the job position. Our model achieves better F1-scores than previous approaches
for each modality (verbal content, audio and video). Results from early and
late multimodal fusion suggest that more sophisticated fusion schemes are
needed to improve on the monomodal results. Finally, some examples of moments
captured by the attention mechanisms suggest our model could potentially be
used to help finding key moments in an asynchronous job interview.
