The paper focuses on deep learning semantic search algorithms applied in the
HR domain. The aim of the article is developing a novel approach to training a
Siamese network to link the skills mentioned in the job ad with the title. It
has been shown that the title normalization process can be based either on
classification or similarity comparison approaches. While classification
algorithms strive to classify a sample into predefined set of categories,
similarity search algorithms take a more flexible approach, since they are
designed to find samples that are similar to a given query sample, without
requiring pre-defined classes and labels. In this article semantic similarity
search to find candidates for title normalization has been used. A pre-trained
language model has been adapted while teaching it to match titles and skills
based on co-occurrence information. For the purpose of this research fifty
billion title-descriptions pairs had been collected for training the model and
thirty three thousand title-description-normalized title triplets, where
normalized job title was picked up manually by job ad creator for testing
purposes. As baselines FastText, BERT, SentenceBert and JobBert have been used.
As a metric of the accuracy of the designed algorithm is Recall in top one,
five and ten model's suggestions. It has been shown that the novel training
objective lets it achieve significant improvement in comparison to other
generic and specific text encoders. Two settings with treating titles as
standalone strings, and with included skills as additional features during
inference have been used and the results have been compared in this article.
Improvements by 10% and 21.5% have been achieved using VacancySBERT and
VacancySBERT (with skills) respectively. The benchmark has been developed as
open-source to foster further research in the area.

该论文关注应用于人力资源领域的深度学习语义搜索算法，旨在开发一种新颖的方法来训练 Siamese 网络以将招聘广告中提到的技能与职位标题关联起来，并通过基于共现信息的语义相似性搜索来找到用于标题标准化的候选人。通过比较 FastText、BERT、SentenceBert 和 JobBert 等基准模型，已经证明了该新方法相对于其他通用文本编码器在准确率上的显著改进，通过 VacancySBERT 和 VacancySBERT（带技能）分别实现了 10% 和 21.5% 的改进，该论文还开发了一个开源的基准模型，以促进该领域的进一步研究。