# MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations

arXiv: https://arxiv.org/abs/2410.08319 · HTML: https://mlsanity.com/paper/2410.08319/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 50 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Builds on (most cited first, 23 of 23)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Improving Text Embeddings with Large Language Models](https://mlsanity.com/paper/2401.00368/) (2023)
- [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [Entity Linking in 100 Languages](https://mlsanity.com/paper/2011.02690/) (2020)
- [SkillSpan: Hard and Soft Skill Extraction from English Job Postings](https://mlsanity.com/paper/2204.12811/) (2022)
- [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://mlsanity.com/paper/2402.16829/) (2024)
- [JobBERT: Understanding Job Titles through Skills](https://mlsanity.com/paper/2109.09605/) (2021)
- [Extreme Multi-Label Skill Extraction Training using Large Language Models](https://mlsanity.com/paper/2307.10778/) (2023)
- [Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction](https://mlsanity.com/paper/2209.05987/) (2022)
- [ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain](https://mlsanity.com/paper/2305.12092/) (2023)
- [Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning](https://mlsanity.com/paper/2205.01381/) (2022)
- [Skill Extraction from Job Postings using Weak Supervision](https://mlsanity.com/paper/2209.08071/) (2022)
- [Career Path Prediction using Resume Representation Learning and Skill-based Matching](https://mlsanity.com/paper/2310.15636/) (2023)
- [Job Posting-Enriched Knowledge Graph for Skills-based Matching](https://mlsanity.com/paper/2109.02554/) (2021)
- [Learn to Not Link: Exploring NIL Prediction in Entity Linking](https://mlsanity.com/paper/2305.15725/) (2023)
- [VacancySBERT: the approach for representation of titles and skills for semantic similarity search in the recruitment domain](https://mlsanity.com/paper/2307.16638/) (2023)
- [Entity Linking in the Job Market Domain](https://mlsanity.com/paper/2401.17979/) (2024)
- [JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning](https://mlsanity.com/paper/2202.10739/) (2022)
- [Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules](https://mlsanity.com/paper/2308.16770/) (2023)
- [Flexible Job Classification with Zero-Shot Learning](https://mlsanity.com/paper/2209.12678/) (2022)

## Built on by (most cited first, 6 of 6)

- [Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2507.13275/) (2025)
- [WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain](https://mlsanity.com/paper/2604.13055/) (2026)
- [Unified Work Embeddings: Contrastive Learning of a Bidirectional Multi-task Ranker](https://mlsanity.com/paper/2511.07969/) (2025)
- [NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching](https://mlsanity.com/paper/2506.19058/) (2025)
- [Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2606.31692/) (2026)
- [TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2607.20009/) (2026)

## Abstract (arXiv)

We present the Multilingual Entity Linking of Occupations (MELO) Benchmark, a new collection of 48 datasets for evaluating the linking of entity mentions in 21 languages to the ESCO Occupations multilingual taxonomy. MELO was built using high-quality, pre-existent human annotations. We conduct experiments with simple lexical models and general-purpose sentence encoders, evaluated as bi-encoders in a zero-shot setup, to establish baselines for future research. The datasets and source code for standardized evaluation are publicly available at https://github.com/Avature/melo-benchmark