Papers by Sergio E. Zanotto
Linguistic and Embedding-Based Profiling of Texts Generated by Humans and Large Language Models (2025.emnlp-main)
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| Challenge: | Recent studies have focused on using LLMs to classify text as either human-written or machine-generated . |
| Approach: | They characterize human-written and machine-generated texts using a set of linguistic features across different linguistic levels such as morphology, syntax, and semantics. |
| Outcome: | The proposed model reveals that human-written texts exhibit simpler syntactic structures and more diverse semantic content. |
GRIT: A Dataset of Group Reference Recognition in Italian (2024.lrec-main)
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| Challenge: | a task of automatically recognizing group references has not yet gained much attention within NLP. |
| Approach: | They propose a large-scale dataset for automatic group reference recognition in italian . they verify the validity of the task using a fine-tuned BERT model . |
| Outcome: | The proposed dataset proves that it can be applied to political text analysis and social media analysis. |
Your Stereotypical Mileage May Vary: Practical Challenges of Evaluating Biases in Multiple Languages and Cultural Contexts (2024.lrec-main)
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Karen Fort, Laura Alonso Alemany, Luciana Benotti, Julien Bezançon, Claudia Borg, Marthese Borg, Yongjian Chen, Fanny Ducel, Yoann Dupont, Guido Ivetta, Zhijian Li, Margot Mieskes, Marco Naguib, Yuyan Qian, Matteo Radaelli, Wolfgang S. Schmeisser-Nieto, Emma Raimundo Schulz, Thiziri Saci, Sarah Saidi, Javier Torroba Marchante, Shilin Xie, Sergio E. Zanotto, Aurélie Névéol
| Challenge: | Recent studies have identified a gap in the availability of tools and resources to study bias in languages other than English and social contexts outside the north of America. |
| Approach: | They use stereotypes to build a corpus of sentence pairs that cover biases in seven cultural contexts. |
| Outcome: | The proposed resource covers a wide range of languages and cultural settings . it favors sentences that express stereotypes in most bias categories . |