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In this paper we focus on constructing useful embeddings of textualinformation in vacancies and resumes, which we aim to incorporate as featuresinto job to job seeker matching models alongside other features. We explain ourtask where noisy data from parsed resumes, heterogeneous nature of thedifferent sources of data, and crosslinguality and multilinguality presentdomain-specific challenges.  We address these challenges by fine-tuning a Siamese Sentence-BERT (SBERT)model, which we call conSultantBERT, using a large-scale, real-world, and highquality dataset of over 270,000 resume-vacancy pairs labeled by our staffingconsultants. We show how our fine-tuned model significantly outperformsunsupervised and supervised baselines that rely on TF-IDF-weighted featurevectors and BERT embeddings. In addition, we find our model successfullymatches cross-lingual and multilingual textual content.

conSultantBERT: Fine-tuned Siamese Sentence-BERT for Matching Jobs and Job Seekers