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Universal Anaphora: The First Three Years

Paper · 2024 · Massimo Poesio, Maciej Ogrodniczuk, Vincent Ng et al.

Summary

Coreference work in NLP has long revolved around OntoNotes. That corpus centres on identity coreference in a limited set of genres and leaves out singletons, expletives and many harder phenomena. Universal Anaphora (UA) began in 2020 and is modelled on Universal Dependencies. It aims to coordinate research on bridging, discourse deixis, split-antecedent plurals, zero anaphora and dialogue reference. Until this paper, the initiative was described only in an online draft from December 2020.

The authors report on the first three years:

  • a catalogue of the anaphoric phenomena annotated across existing corpora, with some marked as required and the rest optional;
  • two markup proposals for encoding them;
  • a collection of corpora in a shared format;
  • the UA scorer, which extends the CoNLL scorer beyond identity anaphora;
  • close cooperation with CorefUD, which adds coreference to UD treebanks;
  • three shared tasks.

They judge the initial goals largely met but flag open issues, notably dialogue markables and how to encode zeros. The work matters because shared formats and evaluation make results comparable across languages and phenomena.

Key points

  • UA has four aims: widen what can be reliably annotated, unify annotation and encoding standards, release datasets in those standards, and build evaluation methods.
  • The core Identity layer recommends marking every nominal phrase, including singletons. Optional attributes include SemType (referring, predicative or expletive) and ARRAU's GENERIC.
  • Corpora differ on what counts as a markable:
    • OntoNotes skips generic bare plurals and allows only proper-noun premodifiers as markables.
    • ARRAU, GUM and GENTLE annotate both of these; UA permits prenominal markables.
  • The UA scorer (1.0 at LREC 2022, 2.0 at IWCS 2023) builds on the reference CoNLL scorer. It also evaluates split antecedents, non-referring expressions, bridging and discourse deixis. It was revised to handle discontinuous markables but supports only one format for zero anaphora.
  • The work links to CorefUD and to shared tasks such as CODI/CRAC 2022 (anaphora, bridging and discourse deixis in dialogue) and the CRAC 2022 multilingual coreference task.

Interesting ideas

  • I1. The spec fixes a small required core and leaves many phenomena optional, so corpora with different coverage can still share one format.
  • I2. A per-mention SemType label separates referring nominals from predicate nominals and expletives, and the scorer measures whether systems get this right.
  • I3. Mentions are allowed to be discontinuous, as when a dialogue partner's acknowledgment splits one referring expression across turns.
  • I4. Some corpora encode zero anaphora through an existing dependency layer instead of through surface spans.
  • I5. The new scorer extends the standard CoNLL scorer rather than replacing it, so classic identity scores stay comparable while extra phenomena get their own metrics.

For Mova

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

  • I1 (frames): The coreference module in prag could use a mandatory identity layer plus optional bridging or deixis layers, each switched on and reported separately.
  • I2 (frames): Labelling "it" in "it seems" as expletive and "a busy place" as predicative would stop the frame builder from creating spurious entities.
  • I3 (world): Representing an entity's mention as a set of spans, not one contiguous subtree, would let dialogue texts feed clean entities into the world model.
  • I4 (tree): Implicit subjects of infinitives or ellipsis sites could be stored as empty nodes on the UD tree, giving coreference a concrete anchor for unrealized arguments.
  • I5 (learn): Mova's coreference evaluation could report standard CoNLL-style numbers next to separate scores for split antecedents and non-referring detection, keeping regressions visible.

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 · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

🧭 Topics 1Research subjects and languages the work is about.Coreference resolution · 575 works

Cite

Cite as Massimo Poesio, Maciej Ogrodniczuk, Vincent Ng, Sameer Pradhan, Juntao Yu, Nafise Sadat Moosavi, Silviu Paun, Amir Zeldes, Anna Nedoluzhko, Michal Novák, Martin Popel, Zdeněk Žabokrtský and Daniel Zeman. 2024. Universal Anaphora: The First Three Years. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{poesio2024universal,
  title = {{Universal Anaphora: The First Three Years}},
  author = {Massimo Poesio, Maciej Ogrodniczuk, Vincent Ng, Sameer Pradhan, Juntao Yu, Nafise Sadat Moosavi, Silviu Paun, Amir Zeldes, Anna Nedoluzhko, Michal Novák, Martin Popel, Zdeněk Žabokrtský and Daniel Zeman},
  booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  year = {2024},
  doi = {10.63317/3vy7r5racpcx},
  url = {https://doi.org/10.63317/3vy7r5racpcx}
}

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