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The Validity of Coreference-based Evaluations of Natural Language Understanding

Paper · 2026 · Ian Porada

Summary

This McGill PhD thesis asks what we can actually conclude when NLP systems are scored on coreference resolution, a task long used as a proxy for language understanding. The author applies measurement-theory concepts to coreference benchmarks such as OntoNotes and its derivatives, and to Winograd-style challenge sets. Three validity problems emerge. First, datasets disagree about what counts as coreference (contestedness). Second, benchmarks rank the same models differently (weak convergent validity). Third, gains credited to new architectures may really come from the size of the underlying language model (weak discriminant validity). A controlled re-evaluation of encoder- and decoder-based coreference systems supports the third point. The thesis then builds a new evaluation of event plausibility, an inference that coreference depends on. It checks whether a model's judgments stay consistent across a conceptual hierarchy. Modern language models score well on standard benchmarks but behave inconsistently once contexts are slightly changed. The work matters because it shows that leaderboard gains on coreference do not automatically mean robust, generalizable understanding.

Key points

  • Four peer-reviewed papers form the core of the thesis:
    • a CoNLL 2024 paper examining whether Winograd schemas really measure the resolution of pronoun coreference;
    • a Findings of ACL 2024 paper taking a measurement-modeling view of generalization;
    • an LREC-COLING 2024 controlled re-evaluation of coreference models;
    • a NAACL-HLT 2021 paper on event plausibility with consistent conceptual abstraction.
  • Systems can do well on a Winograd challenge set without truly solving pronominal coreference resolution (PCR). The author also shows that ensembling systems raises evaluation scores.
  • The study of OntoNotes-style benchmarks identifies three problems: competing definitions of coreference, model rankings that change across datasets, and confusion between architectural novelty and language-model scale.
  • In the controlled re-evaluation, encoder-based and decoder-based architectures were reimplemented in one shared framework, so they could be compared under the same conditions.
  • The plausibility evaluation shows that language-model-based systems give inconsistent judgments for the same event at different levels of a semantic hierarchy.

Interesting ideas

  • I1. Treat a benchmark as a measuring instrument and ask three questions: is the construct contested, do rankings agree across datasets, and can the gain be explained by a confound like model size? This audits what a score really means.
  • I2. Re-run competing systems inside one shared framework with the same backbone, so that the measured differences come from the method itself rather than setup differences.
  • I3. Test plausibility consistency along a concept hierarchy: if an event is plausible for a specific noun, the judgment for its hypernym should agree. This reveals models that judge each phrasing in isolation.
  • I4. Challenge sets can be passed through shortcuts, so a high score on a narrow set is not proof of competence. Results should be cross-checked against broad, natural-text evaluations.
  • I5. Slightly perturbing the evaluation context is a cheap way to expose fragile generalization that standard benchmarks miss.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Before trusting a gain, check whether it holds across several test sets and whether it survives when the parser or teacher size is held fixed.
  • I2 (learn): Compare the perceptron parser and gparse variants or repair rules under identical data and settings, so each improvement is attributed to the right component.
  • I3 (world): Use WordNet hypernym chains to check that plausibility constraints on frame arguments stay coherent from specific to general classes.
  • I4 (frames): Evaluate Mova's coreference both on Winograd-style pairs and on running text from fables, so the resolver is not tuned only to puzzle patterns.
  • I5 (answer): Build minimally modified versions of school-test and fable questions as negative controls, and check that answers change only when the meaning changes.

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

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Evaluation & benchmarks · 1,220 worksCoreference resolution · 574 works

Cite

Cite as Ian Porada. 2026. The Validity of Coreference-based Evaluations of Natural Language Understanding. arXiv:2602.16200.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2602.16200 · arXiv 2602.16200

@misc{porada2026validity,
  title = {{The Validity of Coreference-based Evaluations of Natural Language Understanding}},
  author = {Ian Porada},
  year = {2026},
  doi = {10.48550/arxiv.2602.16200},
  eprint = {2602.16200},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2602.16200}
}

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