Learning to Match Job Candidates Using Multilingual Bi-Encoder BERT

Abstract

In this talk, we will show how we used Randstad history of candidate placements to generate labeled CV-vacancy pairs dataset. Afterwards we fine-tune a multilingual BERT with bi encoder structure over this dataset, by adding a cosine similarity log loss layer. We will explain how using the mentioned structure helps us overcome most of the challenges described above, and how it enables us to build a maintainable and scalable pipeline to match CVs and vacancies. In addition, we show how we gain a better semantic understanding, and learn to bridge the vocabulary gap. Finally, we highlight how multilingual transformers help us handle cross language barrier and might reduce discrimination.

arXiv comment2 pages, To be presented as a main talk at RecSys '21: Fifteenth ACM Conference on Recommender Systems. arXiv admin note: substantial text overlap with arXiv:2109.06501