VacancySBERT：用于招聘领域语义相似度搜索的职位名称与技能表示方法
VacancySBERT: the approach for representation of titles and skills for semantic similarity search in the recruitment domain
AI总结：
本文提出 VacancySBERT，通过基于共现信息适配预训练语言模型进行语义相似度搜索，实现招聘广告中职位名称与技能的匹配及归一化，相比基线模型在 Recall 指标上取得显著提升。
摘要：
本文关注应用于人力资源（HR）领域的深度学习语义搜索算法。本文旨在开发一种训练孪生网络的新方法，以将招聘广告中提及的技能与职位名称相关联。研究表明，职位名称归一化过程可以基于分类或相似度比较方法。分类算法致力于将样本分类到预定义的类别集合中，而相似度搜索算法则采用更灵活的方法，因为它们旨在找到与给定查询样本相似的样本，而不需要预定义的类别和标签。本文使用语义相似度搜索来寻找职位名称归一化的候选对象。通过基于共现信息训练预训练语言模型匹配职位名称和技能，对模型进行了适配。为此项研究，收集了五百亿个职位名称-描述对用于训练模型，以及三万三千个职位名称-描述-归一化职位名称三元组（其中归一化职位名称由招聘广告创建者手动挑选）用于测试。使用 FastText、BERT、SentenceBert 和 JobBert 作为基线。使用模型建议的前 1、前 5 和前 10 的 Recall 作为所设计算法准确率的指标。结果表明，与其它通用和特定文本编码器相比，新的训练目标实现了显著改进。本文使用了两种设置，一种将职位名称作为独立字符串处理，另一种在推理期间将包含的技能作为附加特征，并比较了结果。使用 VacancySBERT 和 VacancySBERT（含技能）分别实现了 10% 和 21.5% 的提升。该基准已开源，以促进该领域的进一步研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
