Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework (2020.coling-main)
Copied to clipboard
| Challenge: | 65% of job descriptions miss describing a significant number of relevant skills, a problem we address with a deep learning model based on a multi-label classification problem . |
| Approach: | They propose a deep learning model to learn the set of enumerated job skills associated with a job description. |
| Outcome: | The proposed model improves on an existing baseline solution by over 9% and 7% absolute improvements in terms of recall and normalized discounted cumulative gain. |
Similar Papers
JobXMLC: EXtreme Multi-Label Classification of Job Skills with Graph Neural Networks (2023.findings-eacl)
Copied to clipboard
Nidhi Goyal, Jushaan Kalra, Charu Sharma, Raghava Mutharaju, Niharika Sachdeva, Ponnurangam Kumaraguru
| Challenge: | Existing approaches to predict missing skills are limited to contextual modelling and do not exploit inter-relational structures like job-job and job-skill relationships. |
| Approach: | They propose a skill prediction framework that exploits structural relationships to predict missing skills using job descriptions. |
| Outcome: | The proposed framework outperforms the state-of-the-art approaches by 6% in precision and 3% in recall on real-world recruitment datasets. |
SkillSpan: Hard and Soft Skill Extraction from English Job Postings (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing studies on Skill Extraction (SE) use crowd-sourced labels or annotations from a predefined skill inventory. |
| Approach: | They propose a dataset that contains 14.5K sentences and over 12.5K annotated spans. |
| Outcome: | The proposed model outperforms non-adapted models and single-task outperformed multi-task learning. |
SkiLLens: Recognising and Mapping Novel Skills from Millions of Job Ads Across Europe Using Language Models (2026.eacl-industry)
Copied to clipboard
| Challenge: | Online job ads (OJAs) provide a real-time view of changing demands but require first retrieving skill mentions from unstructured text and then solving the entity linking problem of connecting them to standardized skill taxonomies. |
| Approach: | They propose a multilingual human-in-the-loop pipeline that extracts candidate skills from national OJA corpora using country-specific word embeddings. |
| Outcome: | The proposed pipeline enables timely, multilingual monitoring of emerging skills, supporting agile policy-making and targeted training initiatives. |
Ranking-Based Autoencoder for Extreme Multi-label Classification (N19-1)
Copied to clipboard
| Challenge: | Existing methods to solve label dependency and noisy labeling problems are limited . experimental results show the proposed method is competitive to state-of-the-art methods . |
| Approach: | They propose a deep learning XML method with word-vector-based self-attention followed by ranking-based AutoEncoder architecture to solve these problems. |
| Outcome: | The proposed method is competitive to state-of-the-art methods on benchmark datasets. |
ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain (2023.acl-long)
Copied to clipboard
| Challenge: | Increasing number of NLP benchmarks highlight need for multilingual models for job-related tasks. |
| Approach: | They introduce a language model called ESCOXLM-R that uses domain-adaptive pre-training on the European Skills, Competences, Qualifications and Occupations taxonomy. |
| Outcome: | The proposed model outperforms XLM-R-large on short spans and entity-level and surface-level span-F1 tasks on entity- and surface level. |
QUEST: Efficient Extreme Multi-Label Text Classification with Large Language Models on Commodity Hardware (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Extreme multi-label text classification (EMTC) involves predicting multiple labels from a vast pool of candidates based on a user’s textual query. |
| Approach: | They propose a Quantized and Efficient Learning with Sampling Technique that uses a hash sampling module to reduce the data volume to one-fourth of its original size. |
| Outcome: | Extensive experiments show that QUEST outperforms existing methods while requiring fewer computational resources. |
Learning Job Title Representation from Job Description Aggregation Network (2024.findings-acl)
Copied to clipboard
Napat Laosaengpha, Thanit Tativannarat, Chawan Piansaddhayanon, Attapol Rutherford, Ekapol Chuangsuwanich
| Challenge: | Existing methods for learning job title representation neglect the rich content within the job description. |
| Approach: | They propose a framework for learning job titles through their respective job description and utilize a Job Description Aggregator component to handle the lengthy description and bidirectional contrastive loss. |
| Outcome: | The proposed framework outperforms the skill-based approach on in-domain and out-of-domain settings and achieving a superior performance. |
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems (N19-1)
Copied to clipboard
| Challenge: | Experimental results show that extreme multi-label learning improves label prediction quality by 3% to 5% in three of the 5 tasks and is competitive in the others. |
| Approach: | They propose a submodular maximization framework with linear cost to find informative labels which are most relevant to other labels yet least redundant with each other. |
| Outcome: | The proposed model improves label prediction quality by 3% to 5% in three of the 5 tasks and is competitive in the others. |
Multilingual Detection of Personal Employment Status on Twitter (2022.acl-long)
Copied to clipboard
| Challenge: | Detecting disclosures of individuals’ employment status on social media is a challenging task due to their rarity in a sea of social media content and the variety of linguistic forms used to describe them. |
| Approach: | They propose to use BERT-based classification models to identify five types of disclosures about individuals’ employment status in three languages. |
| Outcome: | The proposed methods achieve significant gains in precision, recall, and diversity of results in real-world settings of extreme class imbalance. |
Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale? (2025.coling-industry)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification, but the vast number of labels can exceed LLMs’ input limits. |
| Approach: | They propose a method that integrates large language models with dense retrieval techniques to overcome these challenges. |
| Outcome: | The proposed methods avoid frequent retraining by leveraging zero-shot and few-shot learning for real-time label assignment. |