Skip to main content>
Get Started

The Brains Behind Avature AI

Our in-house AI experts conduct novel research and continuously advance Avature’s capabilities across three specialized areas:

Machine Learning (ML)

This cross-platform enabling area sits at the heart of our AI innovation, optimizing our models through research and development.

Generative AI

Focused on expanding our platform’s language processing and generation capabilities, they power products such as Avature Copilot and Agentic Automation.

AI Development

Working closely with Generative AI, this area delivers core capabilities of the Avature DNA platform through Search, Skills and Natural Language Processing (NLP) teams.

AI For Talent Management: Our Team’s Research

In addition to partnering with leading academic institutions to tackle HR’s most pressing challenges, we actively contribute to the AI community by publishing original research in peer-reviewed journals and presenting our advancements at scientific conferences.

2024

MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations

MELO offers 48 datasets to evaluate entity linking of occupations across 21 languages using AI models. This benchmark helps standardize multilingual HR data normalization, supporting organizations in streamlining processes and improving global hiring.

View
2024

Intent Classification Methods for Human Resources Chatbots

This paper analyzes intent classification techniques applied to HR chatbots. Besides exploring supervised and unsupervised learning methods, the study proposes a two-stage retrieval pipeline that increases performance and flexibility by allowing the addition of new intents without retraining.

View
2024

Inductive Graph Neural Network for Job-Skill Framework Analysis

This study showcases the power of combining text encoding and graph neural networks to analyze job-skill links, enabling more accurate job and skill recommendations. It excels with structured and unstructured data, even when dealing with unseen skills.

View
2024

Combined Unsupervised and Contrastive Learning for Multilingual Job Recommendation

This study explores a multilingual job recommendation model that improves ranking accuracy across 11 languages. Its two-stage learning approach supports cross-lingual recommendations and outperforms existing methods in aligning job titles globally.

View
2023

Normalization of Education Information in Digitalized Recruitment Processes

In this paper, our team normalizes education information extracted from resumes by transforming it into level/field of study. They also define a new taxonomy for fields of study and show how the process can be applied to candidate-job matching.

View
2023

Resume Parsing as Hierarchical Sequence Labeling: An Empirical Study

Extracting information from a resume is usually a two-stage process. Our ML team proposes using a neural network architecture to tackle both stages simultaneously. The model improves system efficiency and resume parsing accuracy in 7 languages.

View
2022

Learning Job Titles Similarity from Noisy Skill Labels

Other ML models measure the semantic similarity between job titles using supervised learning techniques trained on vast amounts of manually labeled data. We propose an unsupervised representation learning method that is as effective and less costly.

View

Driving AI Standards With TalentCLEF

Avature has spearheaded the creation of TalentCLEF, a pioneering global initiative hosted by the CLEF organization to advance Natural Language Processing (NLP) in HR. As the organizer, we are shaping benchmarks that enhance AI fairness, multilingual adaptability and real-world relevance in hiring. This endeavor reflects Avature’s commitment to shaping the future of HR technology, ensuring AI supports – rather than replaces – human expertise.

Learn More

Additional Resources

Drawing the Blueprint for AI in HR: Insights from Bain, DHL & Unifi

Everyone needs a plan for implementing AI in HR successfully. Read on for tips from experts that will help inform yours.

AI Recruiting Technology with over 80% Efficacy

Understand the impact of AI recruiting technology on talent acquisition, boosting accuracy and reducing manual review time.