Learning to Ask Screening Questions for Job Postings

• URL: http://arxiv.org/abs/2004.14969v1
• Date: Thu, 30 Apr 2020 17:18:17 GMT
• Title: Learning to Ask Screening Questions for Job Postings
• Authors: Baoxu Shi, Shan Li, Jaewon Yang, Mustafa Emre Kazdagli, Qi He
• Abstract summary: We develop a new product where recruiters can ask screening questions online so that they can filter qualified candidates easily. To add screening questions to all $20$M active jobs at LinkedIn, we propose a new task that aims to automatically generate screening questions for a given job posting. Since this is a new product with no historical data, we employ deep transfer learning to train complex models with limited training data.
• Score: 14.277596274680617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: At LinkedIn, we want to create economic opportunity for everyone in the global workforce. A critical aspect of this goal is matching jobs with qualified applicants. To improve hiring efficiency and reduce the need to manually screening each applicant, we develop a new product where recruiters can ask screening questions online so that they can filter qualified candidates easily. To add screening questions to all $20$M active jobs at LinkedIn, we propose a new task that aims to automatically generate screening questions for a given job posting. To solve the task of generating screening questions, we develop a two-stage deep learning model called Job2Questions, where we apply a deep learning model to detect intent from the text description, and then rank the detected intents by their importance based on other contextual features. Since this is a new product with no historical data, we employ deep transfer learning to train complex models with limited training data. We launched the screening question product and our AI models to LinkedIn users and observed significant impact in the job marketplace. During our online A/B test, we observed $+53.10\%$ screening question suggestion acceptance rate, $+22.17\%$ job coverage, $+190\%$ recruiter-applicant interaction, and $+11$ Net Promoter Score. In sum, the deployed Job2Questions model helps recruiters to find qualified applicants and job seekers to find jobs they are qualified for.

Related papers

• Nasdaq-100 Companies' Hiring Insights: A Topic-based Classification Approach to the Labor Market [0.0]
  We propose a data mining-based approach for job classification in the modern online labor market. Among all 13 job categories, Marketing, Branding, and Sales; Software Engineering; Hardware Engineering; Industrial Engineering; and Project Management are the most frequently posted job classifications.
  arXiv  Detail & Related papers  (2024-09-01T08:18:56Z)
• CLARINET: Augmenting Language Models to Ask Clarification Questions for Retrieval [52.134133938779776]
  We present CLARINET, a system that asks informative clarification questions by choosing questions whose answers would maximize certainty in the correct candidate. Our approach works by augmenting a large language model (LLM) to condition on a retrieval distribution, finetuning end-to-end to generate the question that would have maximized the rank of the true candidate at each turn.
  arXiv  Detail & Related papers  (2024-04-28T18:21:31Z)
• FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation [92.43001160060376]
  We study the factuality of large language models (LLMs) in the context of answering questions that test current world knowledge. We introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types. We benchmark a diverse array of both closed and open-source LLMs under a two-mode evaluation procedure that allows us to measure both correctness and hallucination. Motivated by these results, we present FreshPrompt, a simple few-shot prompting method that substantially boosts the performance of an LLM on FreshQA.
  arXiv  Detail & Related papers  (2023-10-05T00:04:12Z)
• WebCPM: Interactive Web Search for Chinese Long-form Question Answering [104.676752359777]
  Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. We introduce WebCPM, the first Chinese LFQA dataset. We collect 5,500 high-quality question-answer pairs, together with 14,315 supporting facts and 121,330 web search actions.
  arXiv  Detail & Related papers  (2023-05-11T14:47:29Z)
• Detecting Fake Job Postings Using Bidirectional LSTM [0.0]
  This study employs a Bidirectional Long Short-Term Memory (Bi-LSTM) model to identify fake job advertisements. The proposed model demonstrates a superior performance, achieving a 0.91 ROC AUC score and a 98.71% accuracy rate. The findings of this research contribute to the development of robust, automated tools that can help combat the proliferation of fake job postings.
  arXiv  Detail & Related papers  (2023-04-03T20:05:27Z)
• EZInterviewer: To Improve Job Interview Performance with Mock Interview Generator [60.2099886983184]
  EZInterviewer aims to learn from the online interview data and provides mock interview services to the job seekers. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs.
  arXiv  Detail & Related papers  (2023-01-03T07:00:30Z)
• AI-enabled exploration of Instagram profiles predicts soft skills and personality traits to empower hiring decisions [0.0]
  We show that a wide range of behavioral competencies consisting of 16 in-demand soft skills can be automatically predicted from Instagram profiles. Models were built based on a sample of 400 Iranian volunteer users who answered an online questionnaire and provided their Instagram usernames. Deep learning models mostly outperformed by demonstrating 70% and 69% average Accuracy in two-level and three-level classifications respectively.
  arXiv  Detail & Related papers  (2022-12-14T07:35:11Z)
• Template-based Recruitment Email Generation For Job Recommendation [21.265844662228847]
  This work aims at defining the topic of automatic email generation for job recommendation, identifying the challenges, and providing a baseline template-based solution for Danish jobs. We wrap up by discussing the future research directions for better solving this task.
  arXiv  Detail & Related papers  (2022-12-06T11:19:45Z)
• Inquisitive Question Generation for High Level Text Comprehension [60.21497846332531]
  We introduce INQUISITIVE, a dataset of 19K questions that are elicited while a person is reading through a document. We show that readers engage in a series of pragmatic strategies to seek information. We evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions.
  arXiv  Detail & Related papers  (2020-10-04T19:03:39Z)
• Mining Implicit Relevance Feedback from User Behavior for Web Question Answering [92.45607094299181]
  We make the first study to explore the correlation between user behavior and passage relevance. Our approach significantly improves the accuracy of passage ranking without extra human labeled data. In practice, this work has proved effective to substantially reduce the human labeling cost for the QA service in a global commercial search engine.
  arXiv  Detail & Related papers  (2020-06-13T07:02:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.