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Semantic Role Labeling as Syntactic Dependency Parsing

What it is about

Measured on the CoNLL-2012 (OntoNotes) PropBank data converted to Stanford dependencies, over 98% of predicate–argument relations in English and Chinese fall into three tree configurations: the predicate dominates the argument (87% in English), the argument is dominated by the predicate's syntactic dependent in reverse — relative clauses and participles on nouns («broken glass») — or predicate and argument share a governor, through control/raising (xcomp) or coordination. The authors pack the role into the dependency label of each token as a joint label with one slot per configuration (plus a bit saying whether a coordinated predicate shares all its modifiers), so that SRL can be recovered from a labelled tree with over 99% oracle F1 for English. A biaffine dependency parser trained on these joint labels is competitive with the state of the art in span-based SRL. Ablations with oracle parts show that half the remaining errors are in labelling directly dominated arguments, and about a third come from wrong trees.

Ideas

For Mova

20. Syntax: the Universal Dependencies frame, 23-complementation.md (control), 24-coordination-subordination.md, 25-relative-clauses.md, 30-pragmatics-overview.md §4 (from syntax to pragmatics). Code: prag/src/fbench.rs, prag/src/frames.rs, en parser (only for I3). Measures: per-configuration recall on the frame bench, frame bench F1 by role.

Topics: Dependency parsing · Semantic role labeling

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.emnlp-main.610

@inproceedings{shi2020semantic,
  title = {{Semantic Role Labeling as Syntactic Dependency Parsing}},
  author = {Shi, Tianze and Malioutov, Igor and Irsoy, Ozan},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.610},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.610}
}

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