Deep Unordered Composition for Multi-label Classification Applied to Skill Prediction
Résumé
Today, many recruitment processes are digitalized. Job offers are posted on job boards and candidates apply by submitting their resumes. To select an appropriate candidate for a job, recruiters rely mostly on the evaluation of the professional skills of the individual. However, researches have shown that individuals tend to omit some skills from their professional profile. A human recruiter, knowledgeable in a given activity sector, is often able to fill the gaps and infer the missing skills. In this paper our aim is to support this human recruiter by automatically inferring theses missing skills, a non-trivial task. To solve this task, first we propose a method to tackle the skill prediction problem by transforming it from a multi-label classification task it to a binary classification task. Then we implement this method with a deep learning model inspired by the Deep Unordered Composition approach. Two different variants of this model, one with the Deep Averaging Network architecture and the other with the Set-Transformer architecture, are evaluated on an open IT resumes data set, and the results are promising.
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