conSultantBERT: Fine tuned Siamese Sentence BERT for Matching Jobs and Job Seekers
In this paper we focus on constructing useful embeddings of textual information in vacancies and resumes, which we aim to incorporate as features into job to job seeker matching models alongside other features. We explain our task where noisy data from parsed resumes, heterogeneous nature of the different sources of data, and crosslinguality and multilinguality present domain specific challenges. We address these challenges by fine tuning a Siamese SentenceBERT (SBERT) model, which we call conSultantBERT, using a large scale, real world, and high quality dataset of over 270,000 resume vacancy pairs labeled by our staffing consultants. We show how our fine tuned model significantly outperforms unsupervised and supervised baselines that rely on TF IDF weighted feature vectors and BERT embeddings. In addition, we find our model successfully matches cross lingual and multilingual textual content.
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