Mapping Explicit and Implicit Discourse Relations between the RST-DT and the PDTB 3.0

What it is about

On the 365 Wall Street Journal articles annotated in both RST-DT (tree relations over elementary discourse units) and PDTB 3.0 (connective-anchored relations between argument spans), the authors align and map relations empirically. Each PDTB argument is aligned with the RST segment that maximises character overlap with fewest extra characters (85% of continuous arguments align perfectly, 50% one-to-one with a single EDU; discontinuous ones only 47%). A PDTB relation is then mapped to the lowest RST relation covering both aligned segments, checked by the strong nuclearity principle (following nucleus paths to see that each argument lies on its segment's nucleus); relations embedded inside one EDU or reaching a multinuclear node are left unmapped. 80% of relations align (implicit 86.7%, explicit 73.7%, the latter hurt by discontinuous arguments). Explicit relations map more unambiguously to RST labels than implicit ones.

Ideas

• I1. Reuse the alignment recipe to score Mova's links against GUM's RST/eRST layer (see zeldes-2025-erst-signaled-graph-theory-discourse I1): align each frame to the EDU containing its predicate head (a token-index match on the same tokenisation, simpler than character overlap), map a Mova link to the lowest covering RST relation, and check nuclearity as the paper does. Leave links inside one EDU unscored, as the paper leaves embedded relations unmapped, and report how many there are.
• I2. Score explicit-connective links and adjacency-only links separately: the paper shows explicit relations map cleanly and implicit ones scatter across RST labels. Expect a clean precision figure for because/so/after links and a noisy one for sentence-order next; only the first is a fair yardstick for rule changes.

For Mova

36. Discourse relations and coherence §3–4 (RST and PDTB), §11.6 (eRST in GUM not yet used for measurement). Code: a scorer next to prag/src/fbench.rs reading GUM RST on dev (evaluation only). Measures: link precision by link type and by explicit vs implicit.

Topics: Discourse analysis

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.26615/978-954-452-092-2_039

@inproceedings{costa2023mapping,
  title = {{Mapping Explicit and Implicit Discourse Relations between the RST-DT and the PDTB 3.0}},
  author = {Costa, Nelson Filipe and Sheikh, Nadia and Kosseim, Leila},
  booktitle = {Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing},
  year = {2023},
  doi = {10.26615/978-954-452-092-2\_039},
  url = {https://doi.org/10.26615/978-954-452-092-2_039}
}

References

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