Learning Job Title Representation from Job Description Aggregation Network (2024)

Abstract: Learning job title representation is a vital process for developing automatic human resource tools. To do so, existing methods primarily rely on learning the title representation through skills extracted from the job description, neglecting the rich and diverse content within. Thus, we propose an alternative framework for learning job titles through their respective job description (JD) and utilize a Job Description Aggregator component to handle the lengthy description and bidirectional contrastive loss to account for the bidirectional relationship between the job title and its description. We evaluated the performance of our method on both in-domain and out-of-domain settings, achieving a superior performance over the skill-based approach.

## introduction with the rapid expansion of online recruitment platforms, vast amounts of job advertisement data (jad) have been generated. one key part of this data is a job posting, providing detailed information on job titles, specialties, and responsibilities for open positions. thus, the availability of a system that could understand the post semantics, especially job titles, would greatly facilitate the matchmaking process between both the recruiters and job applicants. this leads to a surge of interest in learning job title representation due to its potential ability to automate job-related tasks such as job recommendation @xcite , job trajectory prediction @xcite , and job title benchmarking @xcite . to learn the title representation, previous works have primarily relied on utilizing skills information to learn the association between the job title and their respective skill @xcite @xcite . however, this approach also has some shortcomings as it requires skill information. the skills for a given job are either manually listed, which can be erroneous or incomplete, or automatically extracted from the job description through methods such as keyword matching or automatic skill extraction @xcite . these skill extraction methods often require a predefined skill vocabulary or a curated dataset @xcite . furthermore, it is necessary to keep these resources up-to-date with trends in the job market as the dynamic and rapid growth of emerging job roles. previous works mitigate these problems by generating synthetic skill data @xcite or creating datasets where both job titles, and skill lists are readily available @xcite . nonetheless, the former approach further increases pipeline complexity, while the latter suffers from missing skills annotation caused by a communication gap between employers and recruiters. in this work, we propose to overcome the challenges of obtaining a comprehensive set of skills by bypassing the whole process and instead develop a new framework to learn job titles directly through job descriptions (jds) without the need for the skill extraction pipeline. we introduce job description aggregation network, which reweights each segment of the jd by their importance and then aggregates them into a unified jd representation. our contributions are as follows: ## our proposed method an overview of the proposed framework is illustrated in figure 1 . initially, job titles and their respective segmented job descriptions are independently fed into a sentence encoder to obtain their representation. subsequently, the sentence embeddings are fused through the job description aggregator to acquire a unified representation. finally, bidirectional contrastive loss is utilized as a training objective to maximize a pairwise similarity between the embedded job title and their respective aggregated job description representation while minimizing others. the subsections describe the sentence encoder, job description aggregator, and contrastive learning process in detail. ## experimental setup we benchmarked the performance of our proposed framework under two settings: in-domain and outof-domain. for the in-domain evaluation, we used our own jtg-jobposting dataset for training and jtg-synonym for evaluation. jtg-jobposting is a private thai-english job posting dataset consisting of 28,844 job postings from, https:// jobtopgun.com , a renowned recruitment website in thailand. the postings include job titles, job descriptions, and skills. we performed benchmarking on the jtg-synonym dataset by posing the problem as a cross-lingual synonym retrieval task where the job titles were used as queries, and all synonyms were used as the candidate pool. each query was performed on the english and thai candidate pools separately to calculate the r@5, r@10, and map@25. the final metric values were obtained by averaging across every querycandidate-pool pairs. this evaluation protocol was intentionally designed to avoid language bias where a query would prefer a candidate from the same language. this issue will be later discussed in the section 4.5. for the out-of-domain evaluation, we used mycareersfuture.sg @xcite , a dataset of real-world job postings collected by the singaporean government as the training set. esco (le vrang et al., 2014), a standardized system of the european union (eu) for categorizing skills, competencies, qualifications and occupations was used as the validation and test data. the task chosen for benchmarking is job normalization where the goal is to predict the standardized version of each job title. the esco job normalization dataset consists of 30,926 unique job titles and 2,675 standardized esco occupation labels. we followed @xcite and used the standard micro-average of recall at 5, 10 and mrr as metrics. the summary statistics of the datasets are shown in ## conclusion in this paper, we propose a framework for learning the semantic similarity of job titles through job descriptions, bypassing the need for a complete set of skills. the job description aggregator and bidirectional contrastive loss are also introduced to handle the nature of lengthy job descriptions and the two-way relationship between the job title and its description. our results show that our approach achieves superior performance over the previous state-of-the-art skill-based methods.