“Am I Answering My Job Interview Questions Right?”: A NLP Approach to Predict Degree of Explanation in Job Interview Responses

Raghu Verrap, Ehsanul Nirjhar, Ani Nenkova, Theodora Chaspari

Abstract
Providing the right amount of explanation in an employment interview can help the interviewee effectively communicate their skills and experience to the interviewer and convince the she/he is the right candidate for the job. This paper examines natural language processing (NLP) approaches, including word-based tokenization, lexicon-based representations, and pre-trained embeddings with deep learning models, for detecting the degree of explanation in a job interview response. These are exemplified in a study of 24 military veterans who are the focal group of this study, since they can experience unique challenges in job interviews due to the unique verbal communication style that is prevalent in the military. Military veterans conducted mock interviews with industry recruiters and data from these interviews were transcribed and analyzed. Results indicate that the feasibility of automated NLP methods for detecting the degree of explanation in an interview response. Features based on tokenizer analysis are the most effective in detecting under-explained responses (i.e., 0.29 F1-score), while lexicon-based methods depict the higher performance in detecting over-explanation (i.e., 0.51 F1-score). Findings from this work lay the foundation for the design of intelligent assistive technologies that can provide personalized learning pathways to job candidates, especially those belonging to sensitive or under-represented populations, and helping them succeed in employment job interviews, ultimately contributing to an inclusive workforce.
Anthology ID:
2022.nlp4pi-1.14
Volume:
Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Laura Biester, Dorottya Demszky, Zhijing Jin, Mrinmaya Sachan, Joel Tetreault, Steven Wilson, Lu Xiao, Jieyu Zhao
Venue:
NLP4PI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
122–129
Language:
URL:
https://aclanthology.org/2022.nlp4pi-1.14
DOI:
10.18653/v1/2022.nlp4pi-1.14
Bibkey:
Cite (ACL):
Raghu Verrap, Ehsanul Nirjhar, Ani Nenkova, and Theodora Chaspari. 2022. “Am I Answering My Job Interview Questions Right?”: A NLP Approach to Predict Degree of Explanation in Job Interview Responses. In Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI), pages 122–129, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
“Am I Answering My Job Interview Questions Right?”: A NLP Approach to Predict Degree of Explanation in Job Interview Responses (Verrap et al., NLP4PI 2022)
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