@inproceedings{hulsing-etal-2026-ipn,
    title = "{IPN} at {MWE}-2026 {PARSEME} 2.0 Subtask 1: {MWE} Identification via Related Languages and Harnessing Thinking Mode",
    author = {H{\"u}lsing, Anna  and
      Michael, Noah-Manuel  and
      Melanchthon, Daniel Mora  and
      Horbach, Andrea},
    editor = {Ojha, Atul Kr.  and
      Mititelu, Verginica Barbu  and
      Constant, Mathieu  and
      Stoyanova, Ivelina  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Rademaker, Alexandre},
    booktitle = "Proceedings of the 22nd Workshop on Multiword Expressions ({MWE} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.mwe-1.24/",
    pages = "177--186",
    ISBN = "979-8-89176-363-0",
    abstract = "We present IPN, our system for Subtask 1 of the PARSEME 2.0 Shared Task, which targets the identification of MWEs in 17 languages. Overall, IPN outperformed a much larger-parameter baseline model, yet a performance gap to the top-performing systems remains. To better understand these results, we investigate Qwen3-32B{'}s suitability for mono-, cross- and multilingual MWE identification. We also explore whether this model benefits from prepending automatically generated thinking data to the gold label during instruction-tuning. We find that target language data is vital for instruction-tuning. Prepending generated thinking data to a subset of the training data slightly improves performance for two out of three languages, but more detailed evaluation is required."
}