NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching

• URL: http://arxiv.org/abs/2506.19058v1
• Date: Mon, 23 Jun 2025 19:18:25 GMT
• Title: NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching
• Authors: Mike Zhang, Rob van der Goot,
• Abstract summary: Matching job titles is a highly relevant task in the computational job market domain, as it improves candidate matching, career path prediction, and job market analysis.<n>In this report, we outline NLPnorth's submission to TalentCLEF 2025, which includes both of these tasks: Multilingual Job Title Matching, and Job Title-Based Skill Prediction.<n>We observe that for Task A, our prompting approach performs best with an average of 0.492 mean average precision (MAP) on test data, averaged over English, Spanish, and German.<n>Overall, we find that the largest multilingual language models perform best for both tasks.
• Score: 14.504657756151397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Matching job titles is a highly relevant task in the computational job market domain, as it improves e.g., automatic candidate matching, career path prediction, and job market analysis. Furthermore, aligning job titles to job skills can be considered an extension to this task, with similar relevance for the same downstream tasks. In this report, we outline NLPnorth's submission to TalentCLEF 2025, which includes both of these tasks: Multilingual Job Title Matching, and Job Title-Based Skill Prediction. For both tasks we compare (fine-tuned) classification-based, (fine-tuned) contrastive-based, and prompting methods. We observe that for Task A, our prompting approach performs best with an average of 0.492 mean average precision (MAP) on test data, averaged over English, Spanish, and German. For Task B, we obtain an MAP of 0.290 on test data with our fine-tuned classification-based approach. Additionally, we made use of extra data by pulling all the language-specific titles and corresponding \emph{descriptions} from ESCO for each job and skill. Overall, we find that the largest multilingual language models perform best for both tasks. Per the provisional results and only counting the unique teams, the ranking on Task A is 5$^{\text{th}}$/20 and for Task B 3$^{\text{rd}}$/14.

Related papers

• Cross-lingual Contextualized Phrase Retrieval [63.80154430930898]
  We propose a new task formulation of dense retrieval, cross-lingual contextualized phrase retrieval. We train our Cross-lingual Contextualized Phrase Retriever (CCPR) using contrastive learning. On the phrase retrieval task, CCPR surpasses baselines by a significant margin, achieving a top-1 accuracy that is at least 13 points higher.
  arXiv  Detail & Related papers  (2024-03-25T14:46:51Z)
• Unify word-level and span-level tasks: NJUNLP's Participation for the WMT2023 Quality Estimation Shared Task [59.46906545506715]
  We introduce the NJUNLP team to the WMT 2023 Quality Estimation (QE) shared task. Our team submitted predictions for the English-German language pair on all two sub-tasks. Our models achieved the best results in English-German for both word-level and fine-grained error span detection sub-tasks.
  arXiv  Detail & Related papers  (2023-09-23T01:52:14Z)
• VacancySBERT: the approach for representation of titles and skills for semantic similarity search in the recruitment domain [0.0]
  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.
  arXiv  Detail & Related papers  (2023-07-31T13:21:15Z)
• Automated Few-shot Classification with Instruction-Finetuned Language Models [76.69064714392165]
  We show that AuT-Few outperforms state-of-the-art few-shot learning methods. We also show that AuT-Few is the best ranking method across datasets on the RAFT few-shot benchmark.
  arXiv  Detail & Related papers  (2023-05-21T21:50:27Z)
• A practical method for occupational skills detection in Vietnamese job listings [0.16114012813668932]
  Lack of accurate and timely labor market information leads to skill miss-matches. Traditional approaches rely on existing taxonomy and/or large annotated data. We propose a practical methodology for skill detection in Vietnamese job listings.
  arXiv  Detail & Related papers  (2022-10-26T10:23:18Z)
• Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation [80.16548523140025]
  We extend the vanilla pretrain-finetune pipeline with extra code-switching restore task to bridge the gap between the pretrain and finetune stages. Our approach could narrow the cross-lingual sentence representation distance and improve low-frequency word translation with trivial computational cost.
  arXiv  Detail & Related papers  (2022-04-16T16:08:38Z)
• Predicting Job Titles from Job Descriptions with Multi-label Text Classification [0.0]
  We propose the multi-label classification approach for predicting relevant job titles from job description texts. We implement the Bi-GRU-LSTM-CNN with different pre-trained language models to apply for the job titles prediction problem.
  arXiv  Detail & Related papers  (2021-12-21T09:31:03Z)
• Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning [51.34011135329063]
  Job Title Benchmarking (JTB) aims at matching job titles with similar expertise levels across various companies. Traditional JTB approaches mainly rely on manual market surveys, which is expensive and labor-intensive. We reformulate the JTB as the task of link prediction over the Job-Graph that matched job titles should have links.
  arXiv  Detail & Related papers  (2020-09-16T02:33:32Z)
• Adaptive Task Sampling for Meta-Learning [79.61146834134459]
  Key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time. We propose an adaptive task sampling method to improve the generalization performance.
  arXiv  Detail & Related papers  (2020-07-17T03:15:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.