Papers by Ricardo Muñoz Sánchez
Intrinsic Bias Metrics Do Not Correlate with Application Bias (2021.acl-long)
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| Challenge: | a recent survey of bias in natural language processing found that a coreference system makes more errors in an anti-stereotypical coreferent than in a pro-sterereotype one. |
| Approach: | They compare intrinsic and extrinsic bias metrics across hundreds of trained models . they urge researchers to focus on extrindic measures of bias, not easy to measure . |
| Outcome: | a new intrinsic metric and an annotated test set on gender bias in hate speech are tested . authors urge researchers to focus on extrinsic measures of bias, and to make them more feasible . |
Pseudonymization Categories across Domain Boundaries (2024.lrec-main)
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Maria Irena Szawerna, Simon Dobnik, Therese Lindström Tiedemann, Ricardo Muñoz Sánchez, Xuan-Son Vu, Elena Volodina
| Challenge: | Linguistic data can contain personal information, which is limited in accessibility . a universal system of tags for categorizing PIIs could be developed to replace them . |
| Approach: | They analyze tagsets used for anonymization and pseudonymization to find out what kinds of PII appear in different domains. |
| Outcome: | The proposed system would allow for dynamic pseudonymization while keeping the data readable and useful for future research. |
UniversalCEFR: Enabling Open Multilingual Research on Language Proficiency Assessment (2025.emnlp-main)
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Joseph Marvin Imperial, Abdullah Barayan, Regina Stodden, Rodrigo Wilkens, Ricardo Muñoz Sánchez, Lingyun Gao, Melissa Torgbi, Dawn Knight, Gail Forey, Reka R. Jablonkai, Ekaterina Kochmar, Robert Joshua Reynolds, Eugénio Ribeiro, Horacio Saggion, Elena Volodina, Sowmya Vajjala, Thomas François, Fernando Alva-Manchego, Harish Tayyar Madabushi
| Challenge: | Language proficiency research plays a central role in education and often intersects with advances in linguistics and AI. |
| Approach: | They propose a multilingual multidimensional dataset of texts annotated according to the CEFR scale in 13 languages. |
| Outcome: | The proposed dataset supports linguistic features and pretrained models in multilingual CEFR level assessment. |