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Onur Celebi

Onur Celebi has 2 papers in our library, in 2021–2022, most of them on Language models & pretraining, Low-resource languages; often with Alexis Conneau, Beliz Gunel, Edouard Grave.

2 papers in our library · 2021–2022

OpenAlex · h-index 10 · 20 works · 1,258 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1,810 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4208

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

🏢MetaMenlo Park · United States2019–2025 · 9 paperscurrent🏢MetaLondon · United Kingdom2019–2021 · 3 papers🎓Stanford UniversityStanford · United States2020–2021 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 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 2Research subjects and languages the work is about.Language models & pretraining · 1 workLow-resource languages · 1 work

 Works by year

2020s

20221 paperBitext Mining Using Distilled Sentence Representations for Low-Resource Languages20211 paperSelf-training Improves Pre-training for Natural Language Understanding

 Papers

The most cited first (OpenAlex and our library).

  1. Self-training Improves Pre-training for Natural Language Understanding (2021 · 169 citations)
    Self-training uses a teacher model to label unannotated examples for a student model. It usually assumes that unlabeled text from the target task's domain is already available, and that assumption breaks down for many low-resource tasks. The authors ask whether self-training still helps on top of strong…
  2. Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages (2022 · 127 citations)
    Multilingual sentence encoders that try to cover every language in one model have trouble with the long tail of low-resource languages.

All works

3 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. No Language Left Behind: Scaling Human-Centered Machine Translation · original ↗ · no summary yet · arXiv:2207.04672NLLB Team et al.2022 · 1,861 citations
  2. Self-training Improves Pre-training for Natural Language UnderstandingJingfei Du, Edouard Grave, Beliz Gunel, et al.Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) · 169 citations
  3. Bitext Mining Using Distilled Sentence Representations for Low-Resource LanguagesKevin Heffernan, Onur Çelebi, Holger SchwenkFindings of the Association for Computational Linguistics · 127 citations

 Co-authors here

9 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 1,810 times (of the 141,840 papers whose bibliographies we read); 188 citing papers with a page here →.

Updated

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