Especially in large companies, the number of incoming applications is very high. Since not all applicants are suitable, it can be very time-consuming to search through the most suitable candidates through the recruiting database. This results in slow response times for candidates that match the companies' criteria.
The automated evaluation of incoming applications can be solved by implementing a machine learning model, that is able to automatically answer predefined questions, e.g. degree, field of study or job experience, on the basis of CV documents or attachments. The major challenge for such a model is to abstract the questions and the information from the CV and to detect semantic relations and dependencies in order to find the corresponding candidates.
The automated question answering of candidates' CVs can be solved by implementing a model based on the pre-trained word embedding by Google developed BERT model (bidirectional encoder representations from transformers). The key advantage of a BERT model is that it is able to recognize semantically similar words and expressions by considering their context. The pre-trained base model is then specialized by further training with labeled CV documents.
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