# EZInterviewer: To Improve Job Interview Performance with Mock Interview Generator

arXiv: https://arxiv.org/abs/2301.00972 · HTML: https://mlsanity.com/paper/2301.00972/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 38 references that are in the map.
- First posted on arXiv 3 Jan 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [K-BERT: Enabling Language Representation with Knowledge Graph](https://mlsanity.com/paper/1909.07606/) (2019)
- [Towards Conversational Recommendation over Multi-Type Dialogs](https://mlsanity.com/paper/2005.03954/) (2020)
- [Contrastive Learning with Adversarial Perturbations for Conditional Text Generation](https://mlsanity.com/paper/2012.07280/) (2020)
- [Low-Resource Knowledge-Grounded Dialogue Generation](https://mlsanity.com/paper/2002.10348/) (2020)
- [DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder](https://mlsanity.com/paper/1805.12352/) (2018)
- [VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles](https://mlsanity.com/paper/2010.05406/) (2020)
- [Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach](https://mlsanity.com/paper/1812.08947/) (2018)
- [Incremental Transformer with Deliberation Decoder for Document Grounded Conversations](https://mlsanity.com/paper/1907.08854/) (2019)
- [Diversifying Dialog Generation via Adaptive Label Smoothing](https://mlsanity.com/paper/2105.14556/) (2021)
- [Target-aware Abstractive Related Work Generation with Contrastive Learning](https://mlsanity.com/paper/2205.13339/) (2022)
- [There Are a Thousand Hamlets in a Thousand People's Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory](https://mlsanity.com/paper/2204.02624/) (2022)
- [Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation](https://mlsanity.com/paper/2009.09378/) (2020)
- [Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog](https://mlsanity.com/paper/2003.04679/) (2020)
- [Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation](https://mlsanity.com/paper/2005.06128/) (2020)
- [The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement](https://mlsanity.com/paper/2012.07419/) (2020)
- [Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation](https://mlsanity.com/paper/2205.13346/) (2022)
- [Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension](https://mlsanity.com/paper/2012.07410/) (2020)
- [Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce](https://mlsanity.com/paper/2005.08188/) (2020)
- [Knowledge-graph based Proactive Dialogue Generation with Improved Meta-Learning](https://mlsanity.com/paper/2004.08798/) (2020)

## Built on by (most cited first, 6 of 6)

- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [MockLLM: A Multi-Agent Behavior Collaboration Framework for Online Job Seeking and Recruiting](https://mlsanity.com/paper/2405.18113/) (2024)
- [Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach](https://mlsanity.com/paper/2404.13067/) (2024)
- [Designing Conversational AI to Support Think-Aloud Practice in Technical Interview Preparation for CS Students](https://mlsanity.com/paper/2507.14418/) (2025)
- [PolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment](https://mlsanity.com/paper/2607.10310/) (2026)

## Abstract (arXiv)

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.