Learning to Match Job Candidates Using Multilingual Bi-Encoder BERT

Dor Lavi

doi:10.1145/3460231.3474609

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arXiv · 2109.07157

Learning to Match Job Candidates Using Multilingual Bi-Encoder BERT

Dor Lavi

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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.

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Dor Lavi. 2021-09-15. Learning to Match Job Candidates Using Multilingual Bi-Encoder BERT. https://doi.org/10.1145/3460231.3474609

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