@inproceedings{maggini-etal-2026-par,
    title = "Par-{ITA}: Benchmarking {S}eq2{S}eq and {LLM}s on a Human-Supervised Parallel Corpus for {I}talian Hyperpartisan Neutralization",
    author = "Maggini, Michele Joshua  and
      Fomsgaard, S{\{}{\textbackslash}o{\}}ren  and
      Maestroni, Michele  and
      Dias, Ga{\textbackslash}{''}el  and
      Gamallo, Pablo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.253/",
    pages = "5592--5615",
    ISBN = "979-8-89176-390-6",
    abstract = "Neutralizing hyperpartisan content is essential for mitigating online polarization, yet research has largely focused on English. We present Par-ITA, a curated subset from Semeval 2023 task 3, consisting in the first human-supervised parallel corpus for Italian hyperpartisan neutralization of 2,475 paragraph pairs. The dataset is constructed using a rigorous three-stage pipeline: (1) expert-led preliminary selection of LLMs for high-quality generation, (2) human-supervised data production with high editing rates (32{--}68{\%}), and (3) post-hoc human validation. We establish extensive benchmarks for this task across seq2seq and decoder-only architectures, evaluating standard fine-tuning, Direct Preference Optimization (DPO), and in-context learning. Our analysis highlights that while DPO effectively maximizes neutrality scores in seq2seq models, automated evaluators like GPT-4o-mini exhibit systematic biases, specifically over-penalizing sensitive political topics compared to human experts. Par-ITA provides a foundational resource for non-English neutralization and a reproducible framework for developing high-quality datasets in subjective domains."
}