VLDB 2026 Research / reviewers in the wild / expert
Christophe Ropers
dblp:324/2505
· DBLP profile ↗
5ranked-venue papers
0as first author
5since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 5 · 5 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
4 papers |
Language models and text generation · 38% Machine translation · 36% Trustworthy machine learning · 26% |
Topics — the 5 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation
low-resource languages |
0.9 | 1 | 2025 | Linguini: A benchmark for language-agnostic linguistic reasoning · NeurIPS 2025 |
Machine learning › Trustworthy machine learning › fairness
demographic bias |
0.7 | 1 | 2023 | Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale · EMNLP 2023 |
Machine learning › Trustworthy machine learning
fairness |
0.7 | 1 | 2023 | Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale · EMNLP 2023 |
Natural language and speech › Machine translation › neural machine translation
multilingual neural machine translation |
0.3 | 1 | 2025 | BOUQuET : dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation · EMNLP 2025 |
Natural language and speech › Language models and text generation
multilingual language models |
0.2 | 1 | 2023 | Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale · EMNLP 2023 |
Methods — techniques the papers use, named apart from their topics
quality evaluation benchmark · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | BOUQuET : dataset, Benchmark and Open initiative for Universal Quality Evaluation in TranslationabstractPierre Andrews, Mikel Artetxe, Mariano Coria Meglioli, Marta R. Costa-jussà, Joe Chuang, David Dale, Mark Duppenthaler, Nathanial Paul Ekberg, Cynthia Gao, Daniel Edward Licht, Jean Maillard, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Eduardo Sánchez, Ioannis Tsiamas, Arina Turkatenko, Albert Ventayol-Boada, Shireen Yates. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. Pierre Andrews, Mikel Artetxe, Mariano Coria Meglioli, Marta R. Costa-jussà, Joe Chuang, David Dale, Mark Duppenthaler, Nathanial Paul Ekberg, Cynthia Gao, Daniel Edward Licht, Jean Maillard, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Eduardo Sánchez, Ioannis Tsiamas, Arina Turkatenko, Albert Ventayol-Boada, Shireen Yates |
EMNLP | 13 |
| 2025 | On the Role of Speech Data in Reducing Toxicity Detection BiasabstractSamuel Bell, Mariano Coria Meglioli, Megan Richards, Eduardo Sánchez, Christophe Ropers, Skyler Wang, Adina Williams, Levent Sagun, Marta R. Costa-jussà. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025. Samuel J. Bell, Mariano Coria Meglioli, Megan Richards, Eduardo Sánchez, Christophe Ropers, Skyler Wang, Adina Williams, Levent Sagun, Marta R. Costa-jussà |
NAACL (Long Papers) | 5 |
| 2025 | Linguini: A benchmark for language-agnostic linguistic reasoningabstractWe propose a new benchmark to measure a language model's linguistic reasoning skills without relying on pre-existing language-specific knowledge. The test covers 894 questions grouped in 160 problems across 75 (mostly) extremely low-resource languages, extracted from the International Linguistic Olympiad corpus. To attain high accuracy on this benchmark, models don't need previous knowledge of the tested language, as all the information needed to solve the linguistic puzzle is presented in the context. We find that, while all analyzed models rank below 25% accuracy, there is a significant gap between open and closed models, with the best-performing proprietary model scoring 24.05% and the best-performing open model 8.84%. Eduardo Sánchez, Belen Alastruey, Christophe Ropers, Arina Turkatenko, Pontus Stenetorp, Mikel Artetxe, Marta R. Costa-jussà |
NeurIPS | 3 |
| 2023 | Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at ScaleabstractMarta Costa-jussà, Pierre Andrews, Eric Smith, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi, Cynthia Gao, Daniel Licht, Carleigh Wood. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023. Marta R. Costa-jussà, Pierre Andrews, Eric Michael Smith, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi, Cynthia Gao, Daniel Licht, Carleigh Wood |
EMNLP | 5 |
| 2023 | HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine TranslationabstractDavid Dale, Elena Voita, Janice Lam, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi, Cynthia Gao, Loic Barrault, Marta Costa-jussà. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023. David Dale, Elena Voita, Janice Lam, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi, Cynthia Gao, Loïc Barrault, Marta R. Costa-jussà |
EMNLP | 5 |