A hierarchical reasoning graph neural network for the automatic scoring of answer transcriptions in video job interviews

We address the task of automatically scoring the competency of candidates based on textual features, from the automatic speech recognition transcriptions in the asynchronous video job interviews. The key challenge is to construct the dependency relations and semantic level interaction over each question–answer (QA) pair. However, most recent studies focus on the representation of questions and answers, 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 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 on CHNAT (a real-world dataset) validate that our proposed model significantly outperforms matching-based benchmark models. Ablation studies and experimental results with 10 random seeds also show the effectiveness and stability of our models.