Papers by Mariano Coria Meglioli
BOUQuET : dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation (2025.emnlp-main)
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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
| Challenge: | BOUQUET is a multi-way, multicentric and multi-register/domain dataset and benchmark . the dataset is handcrafted in 8 non-English languages . |
| Approach: | They propose to use BOUQuET to collect a multi-way, multicentric and multi-register/domain dataset and benchmark in 8 non-English languages. |
| Outcome: | The proposed dataset is available at https://huggingface.co/datasets/facebook/bouquet. |
LCFO: Long Context and Long Form Output Dataset and Benchmarking (2025.findings-acl)
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Marta R. Costa-jussà, Pierre Andrews, Mariano Coria Meglioli, Joy Chen, Joe Chuang, David Dale, Christophe Ropers, Alexandre Mourachko, Eduardo Sánchez, Holger Schwenk, Tuan A. Tran, Arina Turkatenko, Carleigh Wood
| Challenge: | Using long text outputs to evaluate progress in summarization and summary expansion tasks is challenging. |
| Approach: | They propose a framework for assessing gradual summarization and summary expansion capabilities across diverse domains. |
| Outcome: | The proposed framework provides alignments between specific QA pairs and corresponding summaries in 7 domains. |
On the Role of Speech Data in Reducing Toxicity Detection Bias (2025.naacl-long)
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Samuel Bell, Mariano Coria Meglioli, Megan Richards, Eduardo Sánchez, Christophe Ropers, Skyler Wang, Adina Williams, Levent Sagun, Marta R. Costa-jussà
| Challenge: | Text toxicity detection systems produce disproportionate rates of false positives on demographic groups . toxicity classification systems often misinterpret benign group mentions as toxic . |
| Approach: | They use group annotations to compare text-based and speech-based toxicity detection systems. |
| Outcome: | The results show that access to speech data supports reduced bias against group mentions . the authors recommend improving classifiers, rather than transcription pipelines if possible . |