  We address the task of automatically scoring the competency of candidates
based on textual features, from the automatic speech recognition (ASR)
transcriptions in the asynchronous video job interview (AVI). The key challenge
is how to construct the dependency relation between questions and answers, and
conduct the semantic level interaction for each question-answer (QA) pair.
However, most of the recent studies in AVI focus on how to represent questions
and answers better, but ignore the dependency information and interaction
between them, which is critical for QA evaluation. In this work, we propose a
Hierarchical Reasoning Graph Neural Network (HRGNN) for the automatic
assessment of question-answer pairs. Specifically, we construct a
sentence-level relational graph neural network to capture the dependency
information of sentences in or between the question and the answer. Based on
these graphs, we employ a semantic-level reasoning graph attention network to
model the interaction states of the current QA session. Finally, we propose a
gated recurrent unit encoder to represent the temporal question-answer pairs
for the final prediction. Empirical results conducted on CHNAT (a real-world
dataset) validate that our proposed model significantly outperforms
text-matching based benchmark models. Ablation studies and experimental results
with 10 random seeds also show the effectiveness and stability of our models.
