Bootstrapping UMRs from Universal Dependencies for Scalable Multilingual Annotation

What it is about

UMR annotation is slow, so the paper converts Universal Dependencies trees into partial UMR graphs that annotators then correct. The converter is language-independent. Concepts are the UD lemmas. Participant roles are non-lexicalised (actor, undergoer, recipient) and come from rules that combine the UD relation with morphology. Appositions and copulas become abstract predicates such as identity-91. Where UD gives too little, the converter writes placeholders: ASP for aspect, type-NE for entity type, OBLIQUE for a broad group of adjunct relations. The evaluation uses 100 parallel sentences in Czech, English and Italian, manually annotated in UMR. Alignment matching is done by AnCast's automatic anchors, because discontinuous alignments are not supported. Precision is higher than recall. English scores best. Modal strength and inverted relations are the hardest. Under strict scoring, a placeholder counts as wrong even when it names the right group.

Ideas

• I1. Two-level scoring. Score each placeholder twice: exact, and right within its group (for example OBLIQUE). Report both for the translator's own placeholder output, so partial correctness is visible.
• I2. Role table counted from data. Compare the frame roles that prag assigns to a UD-to-role table like the paper's on the PUD sentences. Coverage per relation shows where the rules are missing.
• I3. High precision, low recall. The converter drops triples that would disconnect a graph. Apply the same design to the meta-world: emit only tokens the reader is sure of, and count what was dropped per class.
• I4. Parallel test design. The three-language design, with a shared sentence set, can be used to check whether the meta-world of one verse is the same across its translations.

For Mova

The partial-graph idea is a sound model for a reader that has to say when it does not know. The tables in I2 can be counted in tools/roundtrip and prag, not written by hand. The placeholder scoring in I1 and the drop-rate in I3 belong in tools/translate/src/measure.rs. They touch open problem 1 and open problem 4, since placeholders stand in for function words the reader cannot yet read.

Topics: Universal Dependencies · Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.law-1.10

@inproceedings{gamba2025bootstrapping,
  title = {{Bootstrapping UMRs from Universal Dependencies for Scalable Multilingual Annotation}},
  author = {Gamba, Federica and Palmer, Alexis and Zeman, Daniel},
  booktitle = {Proceedings of the 19th Linguistic Annotation Workshop (LAW-XIX-2025)},
  year = {2025},
  doi = {10.18653/v1/2025.law-1.10},
  url = {https://doi.org/10.18653/v1/2025.law-1.10}
}

References

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