# Learning to Match Job Candidates Using Multilingual Bi-Encoder BERT

arXiv: https://arxiv.org/abs/2109.07157 · HTML: https://mlsanity.com/paper/2109.07157/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Survey Of Cross-lingual Word Embedding Models](https://mlsanity.com/paper/1706.04902/) (2017)
- [Evolution of Semantic Similarity -- A Survey](https://mlsanity.com/paper/2004.13820/) (2020)

## Built on by (most cited first, 1 of 1)

- [Efficient Large-Scale Visual Representation Learning And Evaluation](https://mlsanity.com/paper/2305.13399/) (2023)

## Abstract (arXiv)

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.