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Metaphor Identification in Spanish Oncological Discourse: The Role of Explicit Meaning in Low-Resource Settings

Paper · 2026 · Lucia Pitarch, Jordi Bernad and Gemma Bel-Enguix

Summary

Automatic metaphor detection performs well on large general-domain English benchmarks such as the VUA corpus. It transfers poorly, however, to specialized settings with little labeled data, such as Spanish texts about cancer. The authors ask whether explicit meaning information, the core of the Metaphor Identification Procedure (MIP), helps a model in this situation. They use a filtered portion of FLAVORS-AECC, a Spanish oncological dataset where every instance is labeled with WordNet synsets for both its basic meaning (BM) and its meaning in context (CM). On this data they fine-tune the Contrast-WSD architecture with an XLM-RoBERTa-base encoder. They compare three input variants:

  • no meaning information (control),
  • manually chosen BM,
  • the first WordNet sense as BM.

Each variant is tested on a random split and on a split where train and test share no lemmas.

Adding definitions raises F1 sharply, from below 0.30 for the control to above 0.50 in the best configurations. Unexpectedly, hand-curated BM is not reliably better than the first dictionary sense. The authors suspect that longer definitions add noise. Models do best on high-agreement items and worst on verbs. The study supports linguistically informed modeling for low-resource domains.

Key points

  • Data: 5,239 single-word instances (verbs, nouns and adjectives only).
    • 18% are marked metaphorical by at least one annotator.
    • POS distribution: 64% verbs, 24% nouns, 12% adjectives.
    • Inter-annotator agreement in the original dataset: 0.49 F1.
  • Setup: Contrast-WSD with XLM-RoBERTa-base.
    • The Lesk algorithm replaces an external WSD model.
    • Definitions are in Spanish where available, otherwise in English.
    • Best hyperparameters: class weight 5, learning rate 1e-5, batch size 16, 10 epochs.
    • Results are the mean of 5 seeds with 95% confidence intervals.
  • Lemma-based split (F1):
    • Control: about 0.27–0.29.
    • Manual BM: about 0.34–0.36.
    • First dictionary entry: about 0.36–0.38.
  • Random split (F1):
    • First dictionary entry: about 0.50.
    • Manual BM: 0.50–0.51 (best reported: 0.514).
    • The gap between the two BM strategies is negligible.
  • Error patterns: accuracy is higher on instances with strong annotator agreement, and verbs remain the hardest part of speech.

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

 Institutions

🎓Universidad Nacional Autónoma de MéxicoMexico City · Mexico8 works · 288 citations🎓Universidad de ZaragozaZaragoza · Spain6 works · 5 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksPsychology · 1,649 worksExperimental and Cognitive Psychology · 930 works

🧭 Topics 3Research subjects and languages the work is about.Discourse analysis · 471 worksIdioms & multiword expressions · 222 worksLow-resource languages · 287 works

Cite

Cite as Lucia Pitarch, Jordi Bernad and Gemma Bel-Enguix. 2026. Metaphor Identification in Spanish Oncological Discourse: The Role of Explicit Meaning in Low-Resource Settings. Proceedings of Learning Non-Literal Expressions with Small Data @ LREC 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/2hvq7fqourt3

@inproceedings{pitarch2026metaphor,
  title = {{Metaphor Identification in Spanish Oncological Discourse: The Role of Explicit Meaning in Low-Resource Settings}},
  author = {Lucia Pitarch, Jordi Bernad and Gemma Bel-Enguix},
  booktitle = {Proceedings of Learning Non-Literal Expressions with Small Data @ LREC 2026},
  year = {2026},
  doi = {10.63317/2hvq7fqourt3},
  url = {https://doi.org/10.63317/2hvq7fqourt3}
}

References

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