Papers by Jens Van Nooten
Jump To Hyperspace: Comparing Euclidean and Hyperbolic Loss Functions for Hierarchical Multi-Label Text Classification (2025.coling-main)
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| Challenge: | Hierarchical Multi-Label Text Classification (HMTC) is a challenging machine learning task . a recent study evaluated the effectiveness of Euclidean and hyperbolic loss functions on HMTC . |
| Approach: | They evaluate label-aware and contrastive losses in the Euclidean and hyperbolic space . they find contrastive loss functions are less effective when deployed in the hyperbolical space compared to non-hyperbolic ones . |
| Outcome: | The proposed model improves on four commonly used HMTC datasets. |
In Benchmarks We Trust ... Or Not? (2025.emnlp-main)
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Ine Gevers, Victor De Marez, Jens Van Nooten, Jens Lemmens, Andriy Kosar, Ehsan Lotfi, Nikolay Banar, Pieter Fivez, Luna De Bruyne, Walter Daelemans
| Challenge: | Existing benchmarks for Large Language Models (LLMs) are inadequate and lack a clear solution. |
| Approach: | They propose checklists to cover all aspects of benchmarking issues, both for benchmark creation and usage. |
| Outcome: | The proposed checklists cover all aspects of benchmarking issues, both for benchmark creation and usage. |
MTEB-NL and E5-NL: Embedding Benchmark and Models for Dutch (2026.findings-acl)
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| Challenge: | Recent advances in embedding resources have led to a lack of representation of the Dutch language in multilingual resources. |
| Approach: | They introduce Massive Text Embedding Benchmark for Dutch (MTEB-NL) which includes existing Dutch datasets and newly created ones, covering a wide range of tasks. |
| Outcome: | The proposed models demonstrate strong performance across multiple tasks. |
CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection (2022.coling-1)
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| Challenge: | CoNTACT is a Dutch language model adapted to the domain of COVID-19 tweets . a turbulent vaccine debate has emerged between advocates and opponents of vaccines - a polarization that will continue to influence future views on vaccines. |
| Approach: | They propose a Dutch language model adapted to the domain of COVID-19 tweets . they use 2.8M Dutch COVId-19 related tweets posted in 2021 to test the model . |
| Outcome: | The proposed model shows statistically significant gains over RobBERT on two tasks. |