Abstract: Massive online interview video data provides an important data basis for intelligent interview evaluation. With the spread of the current global epidemic, the demand for online interviews has increased, as well as the intelligent interview evaluation tools. In a structured interview, the interviewer needs to observe the interviewee’s answers based on the evaluation criteria, and form a profile evaluation of the interviewee’s personality traits, communication skills, and leadership, so as to judge whether the interviewee’s characteristics match the position. Among them, personality evaluation is a widely accepted evaluation method among companies. Because personality traits affect people’s language expression, interpersonal communication and other aspects, it is an important reference to assist the interviewer to decide whether an interviewee meets their job requirements. Based on this, a fine-grained interview evaluation method based on the long short term memory (LSTM) and the hierarchical keyword-question attention mechanism (HKQA-LSTM) is proposed, which aims to score the different personality dimensions of the interviewees and obtain a comprehensive interview score based on this. First, we effectively filter out important words and sentences that are closely related to personality traits in the interview dialogue by introducing a keyword attention mechanism. Then, we use keyword-question level attention mechanism and two-stage model learning mechanism on this basis, and fully combine the multi-scale contextual features of the texts expressed by interviewees to accurately predict personality traits. Finally, through the fusion of personality traits, a comprehensive interpretive evaluation result of the interview is obtained. The experimental results based on real interview scene data show that this method can effectively evaluate the interviewees’ different personality traits scores and accurately predict the interviewees’ overall scores.

Key words: keyword attention, fine-grained scoring, interview evaluation, two-stage learning, natural language processing