  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.
