SLIMER-IT: Zero-Shot NER on Italian Language
Andrew Zamai, Leonardo Rigutini, Marco Maggini, Andrea Zugarini

TL;DR
This paper introduces SLIMER-IT, a zero-shot NER approach for Italian that leverages instruction tuning and prompts, outperforming existing models on unseen entity types and addressing limitations of traditional supervised methods.
Contribution
The paper presents SLIMER-IT, the first instruction-tuning based zero-shot NER model for Italian, with a new evaluation framework and demonstrated superior performance on unseen entity tags.
Findings
SLIMER-IT outperforms state-of-the-art models on unseen entity tags.
The evaluation framework effectively measures zero-shot NER in Italian.
Instruction tuning with prompts enhances zero-shot NER capabilities.
Abstract
Traditional approaches to Named Entity Recognition (NER) frame the task into a BIO sequence labeling problem. Although these systems often excel in the downstream task at hand, they require extensive annotated data and struggle to generalize to out-of-distribution input domains and unseen entity types. On the contrary, Large Language Models (LLMs) have demonstrated strong zero-shot capabilities. While several works address Zero-Shot NER in English, little has been done in other languages. In this paper, we define an evaluation framework for Zero-Shot NER, applying it to the Italian language. Furthermore, we introduce SLIMER-IT, the Italian version of SLIMER, an instruction-tuning approach for zero-shot NER leveraging prompts enriched with definition and guidelines. Comparisons with other state-of-the-art models, demonstrate the superiority of SLIMER-IT on never-seen-before entity tags.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSpeech Recognition and Synthesis
