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Wietse de Vries

Wietse de Vries has 2 papers in our library, in 2019–2021, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, English; often with Malvina Nissim, Andreas van Cranenburgh, Arianna Bisazza.

2 papers in our library · 2019–2021

OpenAlex · ORCID · h-index 7 · 28 works · 370 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 255 times in our library · 5 more in our library without a page yet · ⭐ Paper Stars ranking: #3549

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of GroningenGroningen · Netherlands2020–2028 · 22 papersEmployment 2020–2028 · Scientific Staff, Computational Linguistics (CL)current

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 1 workEnglish · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20211 paperAs Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages

2010s

20191 paperBERTje: A Dutch BERT Model

 Papers

The most cited first (OpenAlex and our library).

  1. BERTje: A Dutch BERT Model (2019 · 360 citations)
    Multilingual BERT covers Dutch, but it was trained only on Wikipedia, which is a narrow domain, and it shares its capacity across 104 languages.
  2. As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages (2021 · 91 citations)
    The paper asks whether an English generative model can be reused for other languages instead of training a new one from scratch, which costs a lot of data and compute.

All works

7 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. BERTje: A Dutch BERT ModelarXiv:1912.09582Wietse de Vries, Andreas van Cranenburgh, Arianna Bisazza, et al.2019 · 360 citations
  2. As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other LanguagesFindings of the Association for Computational LinguisticsWietse de Vries, Malvina Nissim2021 · 91 citations
  3. What's so special about BERT's layers? A closer look at the NLP pipeline in monolingual and multilingual modelsFindings of the Association for Computational Linguistics: EMNLP 2020 · original ↗ · no summary yetWietse de Vries, Andreas van Cranenburgh, Malvina Nissim2020 · 90 citations
  4. A Multilingual Approach to Identify and Classify Exceptional Measures against COVID-19Proceedings of the Natural Legal Language Processing Workshop 2021 · original ↗ · no summary yetGeorgios Tziafas et al.2021 · 13 citations
  5. DUMB: A Benchmark for Smart Evaluation of Dutch ModelsProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · original ↗ · no summary yetWietse de Vries, Martijn Wieling, Malvina Nissim2023 · 8 citations
  6. Can Large Language Models Reliably Correct Errors in Low-Resource ASR? A Contamination-Aware Case Study on West FrisianarXiv:2605.19711 · original ↗ · no summary yetYun Hao et al.2026
  7. Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning and Language Identification for Improved Low-resource PerformancearXiv:2502.04883 · original ↗ · no summary yetReihaneh Amooie et al.2025

 Co-authors here

5 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 255 times (of the 141,840 papers whose bibliographies we read); 28 citing papers with a page here →.

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