Multilingual JobBERT for Cross-Lingual Job Title Matching

• URL: http://arxiv.org/abs/2507.21609v1
• Date: Tue, 29 Jul 2025 09:06:09 GMT
• Title: Multilingual JobBERT for Cross-Lingual Job Title Matching
• Authors: Jens-Joris Decorte, Matthias De Lange, Jeroen Van Hautte,
• Abstract summary: JobBERT-V3 is a contrastive learning-based model for cross-lingual job title matching.<n>Our approach extends support to English, German, Spanish, and Chinese by leveraging synthetic translations.<n>JobBERT-V3 achieves consistent performance across both monolingual and cross-lingual settings.
• Score: 5.284778677072807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce JobBERT-V3, a contrastive learning-based model for cross-lingual job title matching. Building on the state-of-the-art monolingual JobBERT-V2, our approach extends support to English, German, Spanish, and Chinese by leveraging synthetic translations and a balanced multilingual dataset of over 21 million job titles. The model retains the efficiency-focused architecture of its predecessor while enabling robust alignment across languages without requiring task-specific supervision. Extensive evaluations on the TalentCLEF 2025 benchmark demonstrate that JobBERT-V3 outperforms strong multilingual baselines and achieves consistent performance across both monolingual and cross-lingual settings. While not the primary focus, we also show that the model can be effectively used to rank relevant skills for a given job title, demonstrating its broader applicability in multilingual labor market intelligence. The model is publicly available: https://huggingface.co/TechWolf/JobBERT-v3.

Related papers

• Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management [0.2276267460638319]
  We present TalentCLEF 2025, the first evaluation campaign focused on skill and job title intelligence.<n>The evaluations included monolingual and cross-lingual scenarios and covered the evaluation of gender bias.<n> TalentCLEF provides the first public benchmark in this field and encourages the development of robust, fair, and transferable language technologies for the labor market.
  arXiv  Detail & Related papers  (2025-07-17T16:33:57Z)
• Multilingual Definition Modeling [1.9409995498330783]
  We use monolingual dictionary data for four new languages (Spanish, French, Portuguese, and German)<n>We test the performance of pre-trained multilingual language models on definition modeling of monosemic words when finetuned on this data.<n>Results show that multilingual language models can perform on-pair with English but cannot leverage potential cross-lingual synergies.
  arXiv  Detail & Related papers  (2025-06-02T09:48:37Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models [107.83158521848372]
  We present textbfTriangular Document-level textbfPre-training (textbfTRIP), which is the first in the field to accelerate the conventional monolingual and bilingual objectives into a trilingual objective with a novel method called Grafting. TRIP achieves several strong state-of-the-art (SOTA) scores on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark, including consistent improvements by up to 3.11 d-BLEU points and 8.9 ROUGE-L points.
  arXiv  Detail & Related papers  (2022-12-15T12:14:25Z)
• 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)
• conSultantBERT: Fine-tuned Siamese Sentence-BERT for Matching Jobs and Job Seekers [2.208694022993555]
  We explain our task where noisy data from parsed resumes, heterogeneous nature of the different sources of data, and crosslinguality and multilinguality present domain-specific challenges. We address these challenges by fine-tuning a Siamese Sentence Siamese-BERT (SBERT) model, which we call conSultantBERT, using a large-scale, real-world, and high quality dataset of over 270,000 resume-vacancy pairs labeled by our staffing consultants. We show how our fine-tuned model significantly outperforms unsupervised and supervised baselines that rely on TF-IDF-weighted feature vectors and BERT embeddings
  arXiv  Detail & Related papers  (2021-09-14T07:57:05Z)
• Towards Fully Bilingual Deep Language Modeling [1.3455090151301572]
  We consider whether it is possible to pre-train a bilingual model for two remotely related languages without compromising performance at either language. We create a Finnish-English bilingual BERT model and evaluate its performance on datasets used to evaluate the corresponding monolingual models. Our bilingual model performs on par with Google's original English BERT on GLUE and nearly matches the performance of monolingual Finnish BERT on a range of Finnish NLP tasks.
  arXiv  Detail & Related papers  (2020-10-22T12:22:50Z)
• Cross-lingual Spoken Language Understanding with Regularized Representation Alignment [71.53159402053392]
  We propose a regularization approach to align word-level and sentence-level representations across languages without any external resource. Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios.
  arXiv  Detail & Related papers  (2020-09-30T08:56:53Z)
• CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP [68.2650714613869]
  We propose a data augmentation framework to generate multi-lingual code-switching data to fine-tune mBERT. Compared with the existing work, our method does not rely on bilingual sentences for training, and requires only one training process for multiple target languages.
  arXiv  Detail & Related papers  (2020-06-11T13:15:59Z)
• A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT [60.9051207862378]
  multilingual BERT works remarkably well on cross-lingual transfer tasks. Datasize and context window size are crucial factors to the transferability. There is a computationally cheap but effective approach to improve the cross-lingual ability of multilingual BERT.
  arXiv  Detail & Related papers  (2020-04-20T11:13:16Z)

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.