Universal Conceptual Cognitive Annotation (UCCA)

What it is about

UCCA is a semantic annotation scheme that tries to abstract away from syntactic form. Each sentence is a directed acyclic graph whose leaves are words, and whose units mark scenes, argument structure of verbs, nouns and adjectives, and the links between them. The argument is that syntactic schemes separate sentences that mean the same thing, such as taking a shower and showering, while treating some sentences that differ in meaning alike, such as taking a shower and taking someone's book, which the Penn Treebank annotates identically. The foundational layer covers every word, so each word belongs to at least one unit, and further layers can be added on top. Results: on 30 PropBank sentences, the converted UCCA structure agrees with PropBank with an unlabelled F-score of 89.4%. The authors also built a UCCA-annotated corpus, report that annotators without a linguistics background learn the scheme quickly, and name parsing and English–French translation as future work.

Ideas

• I1. Make every source word belong to a unit. In a meta-world reading, a word that is not attached to any concept shows up as a counted gap. This gives a coverage residual per verse and per layer, which fits exact mode.
• I2. Use a coarse base layer for the meta-world: scenes, arguments and linkage only, with function words and fine distinctions added as later layers. The encoder can then be tested layer by layer.
• I3. Paraphrase test: take verses that two translations render differently in wording but identically in meaning, and check that the base layer is the same. Report the share that differs and examine a sample.

For Mova

The code is tools/translate/meta.rs, with the layering in tools/roundtrip. The base-layer idea bears on open problem 5 (number and person from UD features), and I3 is a direct test of whether the meta-world is insensitive to paraphrase. The annotation procedure itself is not for Mova.

Topics: Machine translation

Cite

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

@inproceedings{abend2013universal,
  title = {{Universal Conceptual Cognitive Annotation (UCCA)}},
  author = {Abend, Omri and Rappoport, Ari},
  booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2013},
  url = {https://aclanthology.org/P13-1023/}
}

References

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