Linguistic Analysis of Veteran Job Interviews to Assess Effectiveness in Translating Military Expertise to the Civilian Workforce (2025)

Abstract: The ways in which natural language processing (NLP) can inform how veterans can improve effectiveness in translating military experience to workforce utility is underexplored. We design NLP experiments to evaluate the degree of explanation in veteran job interview responses as a proxy for perceived hireability. We examine linguistic and psycholinguistic features, context, and participant variability to investigate the mechanics of effective communication in employee selection. Results yield good performance when distinguishing between varying degrees of explanation in responses using LIWC features, indicating robustness of linguistic feature integration. Classifying Over- and Under-explained responses reflects challenges of class imbalance and the limitations of tested NLP methods for detecting subtleties in overly verbose or concise communication. Our findings have immediate applications for assistive technologies in job interview settings, and broader implications for enhancing automated communication assessment tools and refining strategies for training and interventions in communication-heavy fields.

## introduction the complexity of verbal communication is a fundamental factor in various realms, including psychology, education, and human-computer interaction (hci). the degree to which individuals explain themselves reveals insights into their cognitive processes, social interactions, and personality traits. these factors both explicitly and implicitly define the ways in which speakers are perceived, and are thus essential for assessing candidates in structured job interviews @xcite . the qualifications, background, and training of the majority of military veterans are notably different from job candidates in the general population. many companies acknowledge that hiring veterans is beneficial, as veterans often posses desirable workforce quali-ties that arise from their unique experiences, such as strong work ethics, leadership skills, adaptability, team orientation, and professionalism @xcite . yet, veterans commonly experience persistent employment challenges post-service due to organizational and societal barriers such as lack of transition support, stressful experiences, and perceived discrimination, as well as personal barriers like incongruence between military and civilian culture @xcite . veterans demonstrate distinct verbal communication gaps in explaining their military experience, references, jargon, and specialized skills relative to the workplace @xcite @xcite . industry interviewers are often unaware of these factors @xcite , further exacerbating the problem with negative stereotypes, stigma, and exclusion @xcite . artificial intelligence (ai) enhances a range of individualized assistive tools to address visual, auditory, cognitive, and physical needs @xcite . automated natural language processing (nlp) and understanding can help specific populations communicate and interact with surroundings more effectively and efficiently. one immediate application is intelligent interview training, which provides a suitable environment for individuals to practice and refine relevant verbal and nonverbal behaviors. such training can help participants adapt to cognitively demanding and socially challenging interview situations @xcite . given that employment interviews are an immediate obstacle in the hiring process, ai-powered interview training, augmented with nlp, has potential to identify linguistic and communicative behaviors that may hinder candidates' performance, then suggest precise modifications to improve their communication skills @xcite . previous research in intervention technologies for interview training primarily seeks to investigate and improve social skills and positive personality signals. various games, systems, and virtual reality platforms have been developed to help users improve interview performance and stress levels through simulated interactions, providing feedback on behavioral and emotional cues @xcite @xcite . other work has used multimodal data from asynchronous job interviews, analyzing linguistic, acoustic, and visual signals to predict personality traits, hireability, and communication skills, with factors such as word choice, personal pronoun use, and speech fluency shown to significantly impact interview outcomes @xcite @xcite @xcite . departing from prior studies, we present foundational knowledge to improve interview training with several key contributions to enhance the development of intervention technologies that use nlp. while some related studies have contributed to adaptive solutions for specific populations @xcite , we focus on military veterans, a population encountering distinct difficulties in job interviews. rather than investigating global characteristics of interviewees, such as personality and overall interview outcomes @xcite @xcite , this research provides detailed analysis of turn-level linguistic behaviors that influence verbal communication patterns. we examine dynamic and complex synchronous (instead of static, asynchronous) interactions between interviewers and interviewees. we not only consider interview responses @xcite , but also account for the content of interview questions, context, turn-taking behaviors, and individualized interviewee variability. ## results in terms of a comparison across features, liwc features consistently outperform others. across all setups, the use of liwc features leads to the best or same overall performance. over-explained or under-explained performance (i.e., class1 f1) also benefit notably from liwc, suggesting its utility in handling minority or challenging classes. the baseline model, which does not utilize additional features, consistently underperforms compared to models that incorporate liwc, but tends to perform comparably to other feature sets. notable gaps are observed in class1 f1, where the baseline scores range from 0.00 to 0.50, indicating poor detection of the over-explained and under-explained responses. however, for the case of distinguishing under-explained responses, the baseline often performs no worse than more complex models. models leveraging pos and normalized jargon count features, generally perform similarly to the baseline, with slight improvements in macro f1 and weighted f1 in some cases. for instance, normalized jargon count marginally improves performance over pos in certain cases, but still trails behind the liwc model performance. models using both question and response inputs outperform those using only responses in some configurations. adding question context tends to not improve results significantly for longer responses, but does show some lift when distinguishing between shorter classes, particularly when identifying under-explained responses. this highlights the importance of leveraging the full conversational context for classification tasks with limited information. for text representation methods, we observe bert-based representations do not show a clear advantage for these tasks, possibly due to limited feature integration or insufficient fine-tuning. simpler bow and tf-idf representations yield comparable results, but benefit significantly from feature augmentation like liwc. performance trends across classes indicate that performance for succinct and comprehensive classes, which represent the majority classes, remain high across all setups, with f1 scores consistently above 0.84. this suggests that models can reliably identify less extreme responses regardless of the features used. over-explained and under-explained classes remain challenging, with low f1 scores, particularly in baseline and non-liwc models. this highlights the class imbalance or inherent difficulty in detecting these classes. liwc consistently improves over-explained and under-explained f1 scores, e.g., achieving up to 0.50 in classification of over-explained and 0.21 in under-explained responses. ## limitations and future work a limitation of this study lies in the small data sample. although difficult to obtain given the interpersonal nature of our dataset, further analyses would benefit from a larger, balanced, and more comprehensively diverse population to improve performance, robustness, and generalizability of algorithms for assistive systems. increasingly complex data, features, and models, would present greater computational expense. more advanced classification strategies to capture the linguistic subtleties between comprehensive and over-explained responses or succinct and under-explained responses may possibly require additional data with higher annotator agreement or data augmentation, as well as careful tuning of vectorizers, classifiers, and class weights. future work could explore advanced integration of liwc with deep learning approaches, combined feature sets, or fine-tuning bert embeddings with domain-specific linguistic features to enhance performance. it would be constructive to also investigate the ways in which other linguistic (e.g., reference to military), physical (e.g., body language, posture), and speech (e.g., volume, intonation) factors influence the degree of explanation. future related work should explore these variables in both binary and four-way classification settings. methods employed and results obtained in our work provide a basis for developing technologies that offer personalized, granular interview feedback in real time. as such, a promising direction for future investigation may involve leveraging large language models and chain-of-thought prompting @xcite to design interactive interview training interfaces. specialized applications of further research to narrow communication gaps may extend beyond job interviews to areas like educational assessments and automated dialogue systems. in addition to military veterans, upcoming studies in this space should aim to make interactions more constructive and meaningful for other sensitive groups, such as formerly incarcerated individuals, non-native speakers, and older adults seeking to re-enter the workforce, by tailoring systems to their unique needs. ## conclusion we use nlp to inform the development of personalized training methods and assistive technologies to aid military veterans in their transition to the civilian workforce. this study integrates advanced linguistic features with robust text representation strategies and participant-dependent crossvalidation to detect the degree of explanation in veteran job interview responses. we incorporate liwc features, which analyze the psychological and cognitive dimensions of text, and pos tag-ging, which provides syntactic insights, into the text classification pipeline. these features are combined with traditional bow and tf-idf vectorization and bert embedding methods to create a comprehensive feature set that can capture both surface-level and deep linguistic patterns. we advance prior studies by looking beyond the ways in which personal, social, and behavioral impressions and physical characteristics impact interview outcomes @xcite @xcite . we also extend existing work by not only considering interview responses, but also accounting for the content of the interview question to understand contextual and turn-taking aspects of conversational communication @xcite . classification results from our binary classification experiments reveal that while tested models can generally distinguish between responses with moderate accuracy, correctly identifying certain subclasses within these categories is more challenging, particularly for under-explained responses. the choice of input features as well as text representation methods significantly impact performance, with liwc features generally leading to better overall results. this research will contribute to the eventual development of intelligent training technologies that provide personalized learning and reintegration support through mechanisms such as real-time automatic feedback to optimize veterans' job interview outcomes and improve the workforce.