---
title: "LlaNa at MultiPRIDE — paper by Mercurio et al. (2026)"
description: "The paper addresses a hard case for hate-speech classifiers: distinguishing reclaimed LGBTQ+ slurs… 12 references, cited by 1 paper here."
authors: ["Alessio Mercurio", "Giorgio Talluto", "Irene Siragusa", "Roberto Pirrone"]
year: 2026
venue: "Proceedings of the Ninth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2026)"
source: "https://aclanthology.org/2026.evalita-1.29/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mercurio-2026-llana-multipride-fine-tuning-approach/"
---
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# LlaNa at MultiPRIDE: A Fine-Tuning Approach with Cross-Lingual Augmentation

**Authors:** Alessio Mercurio, Giorgio Talluto, [Irene Siragusa](https://md.research.elamur.ai/free/en/authors/irene-siragusa/) and [Roberto Pirrone](https://md.research.elamur.ai/free/en/authors/roberto-pirrone/) · **Year:** 2026 · **Venue:** [Proceedings of the Ninth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2026)](https://md.research.elamur.ai/free/en/venues/evalita/) ([Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop 2026](https://md.research.elamur.ai/free/en/venues/evalita/2026/)) · **Publisher:** [CEUR Workshop Proceedings](https://md.research.elamur.ai/free/en/publishers/ceur-workshop-proceedings/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.evalita-1.29/) · [PDF](https://aclanthology.org/2026.evalita-1.29.pdf)

**Citations:** 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper addresses a hard case for hate-speech classifiers: distinguishing reclaimed LGBTQ+ slurs, where community members use a derogatory word to express identity or pride, from slurs used as insults. The work was done for the MultiPRIDE shared task at EVALITA 2026. The LlaNa team took part in the Italian subtasks A1 (tweet text only) and B1 (tweet plus the author's user bio). They identified the shortage of positive reclamation examples as the main obstacle and chose a data-centric fix. Reclamation-labelled tweets from the English and Spanish MultiPRIDE sets were machine-translated into Italian with OPUS-MT. The authors preferred OPUS-MT over LLMs because it was less likely to soften or drop slurs. One XLM-RoBERTa-base model with a compact classification head and a class-weighted loss served both subtasks. Augmentation raised internal validation macro F1 in both settings. On the official test set, the system ranked first in B1 and fifth in A1. The results suggest that low-cost cross-lingual augmentation, combined with author-profile context, can help classifiers separate reclamation from abuse when data is scarce.

## Key points
- **Data:** The Italian training set has 1,086 tweets, only 207 positive (reclamation) versus 879 negative, with an 80/20 internal split for development. A1 augmentation drew on English and Spanish positives; B1 augmentation used only the Spanish B2 data.
- **Model and training:** XLM-RoBERTa-base with 256-token inputs feeds the CLS embedding to a 512-unit dense layer, normalization, ReLU and dropout 0.3, then a binary output. Training used AdamW (lr 1e-5, weight decay 0.05), weighted BCE, batch size 8, up to 8 epochs with early stopping, on a single RTX 3060.
- **Validation gains from augmentation (macro F1):** A1 rose from 0.8817 to 0.9131 (+0.0314); B1 rose from 0.9033 to 0.9196 (+0.0163).
- **Official test results:** B1 reached 0.9021, ranking 1st against a baseline of 0.8981. A1 reached 0.8835, ranking 5th against a baseline of 0.8731. In A1 the positive class showed precision of about 0.86 but recall of only about 0.76.
- **Error analysis:** Misclassifications clustered in tweets mixing conflicting sentiments, such as struggle followed by pride. They also occurred in tweets that merely quote or report someone else's use of a slur, which the model wrongly labelled as reclamation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works

🧭 Topics (1): [🌍 Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · 882 works

## Cite

Cite as: Alessio Mercurio, Giorgio Talluto, Irene Siragusa and Roberto Pirrone. 2026. LlaNa at MultiPRIDE: A Fine-Tuning Approach with Cross-Lingual Augmentation. Proceedings of the Ninth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{mercurio2026llana,
  title = {{LlaNa at MultiPRIDE: A Fine-Tuning Approach with Cross-Lingual Augmentation}},
  author = {Alessio Mercurio, Giorgio Talluto, Irene Siragusa and Roberto Pirrone},
  booktitle = {Proceedings of the Ninth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2026)},
  year = {2026},
  url = {https://aclanthology.org/2026.evalita-1.29/}
}
```

- [← previous LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams](https://md.research.elamur.ai/free/en/papers/wu-2026-livelongbench-tackling-long-context-understanding/)
- [next → LLM-INSTRUCT at UZH Shared Task 2026: Constraint-Aware Retrieval and Selective Debate for Paragraph-Level Argument Mining](https://md.research.elamur.ai/free/en/papers/tran-2026-llm-instruct-uzh-shared-task/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/14/)

## References

12 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*