---
title: "FFT-UniBa at Cruciverb-IT — paper by Porcelli et al. (2026)"
description: "This paper describes the FFT-UniBa entry to Cruciverb-IT, an EVALITA 2026 shared task on… 0.63 MRR. 26 references, cited by 1 paper here."
authors: ["Andrea Porcelli", "Filippo Di Gravina", "Emanuele Fontana", "Mattia Curri", "Francesco Damiano Di Gregorio"]
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-1.38/"
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/porcelli-2026-fft-uniba-cruciverb-it-special/"
---
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# FFT-UniBa at Cruciverb-IT: Special Length Tokens and CSP for Italian Crossword Solving

**Authors:** Andrea Porcelli, Filippo Di Gravina, Emanuele Fontana, Mattia Curri and Francesco Damiano Di Gregorio · **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-1.38/) · [PDF](https://aclanthology.org/2026.evalita-1.38.pdf)

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

## Summary
This paper describes the FFT-UniBa entry to Cruciverb-IT, an EVALITA 2026 shared task on Italian crosswords. The task has two parts: producing ranked answers for single clues of known length, and filling entire grids. The authors argue that subword tokenization hides character counts, so language models struggle to meet strict length constraints. For clue answering, they fine-tuned the Italian T5 variant it5-efficient-small-el32. They added length-specific start and end tokens ([SL=L], [EL=L]) to the input and the target, so the model learns during training where a word of a given length should stop. This differs from forcing length only at decoding time. For grid filling, they treated the puzzle as a CSP. Each slot's domain holds the model's top candidates, ranked by perplexity, plus a small dictionary fallback. A seeded depth-first backtracking search solves the grid, with budgets that scale with grid size. Both components clearly beat the task baselines. The work shows that explicit length conditioning helps compact seq2seq models handle the meta-linguistic side of word games.

## Key points
- **Sub-task 1 results:** up to 0.58 Acc@1, 0.75 Acc@10 and 0.63 MRR. The best configuration was trained for 22 epochs and decoded with 1000 beams.
- **Sub-task 2 results:** on the test set, the system reached at most 0.93 accuracy at the character level, 0.85 at the word level and 0.34 for complete grids.
- **Solver budgets:** the number of seeds, model candidates (K) and dictionary candidates (K′) all grow with grid size. K′ is kept much smaller than K so that the system remains neural rather than a dictionary lookup.
- **Candidate-count ablation:**
  - Grid performance mostly levels off after about 200 candidates per clue.
  - Dictionary fallbacks still give a steady gain on the hardest crossings.
- **Clue-type analysis:**
  - The authors labelled test clues with Gemini 3 Flash into four types: definition, wordplay, fill-in-the-blank and cryptic.
  - Adding dictionary training data gave only marginal gains (about +0.01 MRR) and hurt cryptic clues.
  - The authors conclude that reasoning, not lexical coverage, is the main bottleneck.

*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/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works

## Cite

Cite as: Andrea Porcelli, Filippo Di Gravina, Emanuele Fontana, Mattia Curri and Francesco Damiano Di Gregorio. 2026. FFT-UniBa at Cruciverb-IT: Special Length Tokens and CSP for Italian Crossword Solving. 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{porcelli2026fft,
  title = {{FFT-UniBa at Cruciverb-IT: Special Length Tokens and CSP for Italian Crossword Solving}},
  author = {Andrea Porcelli, Filippo {Di Gravina}, Emanuele Fontana, Mattia Curri and Francesco Damiano {Di Gregorio}},
  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-1.38/}
}
```

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## References

26 references, 3 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 31 October 2026*