GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains

What it is about

PDTB, the only large English shallow-discourse corpus, is news from 1989 and not openly licensed. GDTB converts the GUM corpus (16 genres, 235 documents, 228K tokens) into PDTB v3 style, using GUM's eRST relations, their connective signals, gold UD syntax and coreference: explicit relations come from eRST relations signalled by a connective, mapped to PDTB senses by connective and RST label; implicit ones at each allowed junction (adjacent sentences, purpose infinitives, participial clauses, juxtaposed clauses) get a connective predicted by a fine-tuned T5 and are mapped the same way; AltLex, AltLexC (constructions such as auxiliary inversion «had I gone» for if), entity relations, hypophora and NoRel are added by rules. The whole test set and error-prone relation types (background, joint-other) were corrected by hand. Result: 13.6K relations (7,202 explicit, 4,503 implicit, 465 hypophora — three times PDTB's 141), denser per token than PDTB. Classifiers trained on PDTB lose much accuracy on GDTB genres; joint training on both helps.

Ideas

• I1. GDTB is the closest gold to Mova's links: connective-anchored pairs between clauses, with UD trees on the same tokens. Score Mova's cause/next/cond links against GDTB on GUM dev per genre (fiction and conversation first), mapping PDTB senses Contingency.Cause/Purpose → cause, Temporal.Asynchronous → next (with direction), Contingency.Condition → cond. This is a more direct yardstick than eRST trees (see zeldes-2025-erst-signaled-graph-theory-discourse I1); evaluation only.
• I2. Add the AltLexC constructions as cond rules in prag/src/frames.rs: inverted conditionals without if («Had I known …», «Should you need …», «Were he here …»), which UD parses as an advcl with an inverted aux and no mark. Count them in the tales first; then measure on course questions with conditions.
• I3. Use GDTB's 465 hypophora pairs on dev to test the QUD stack's answer attachment (world/src/qud.rs): the share of questions whose GDTB answer span is where world qud pops the question. This measures the gap shown in chapter 36 example 7 (a because-fragment not accepted as the answer).
• I4. The conversion keeps an and with sequence meaning as two relations (explicit conjunction + implicit then precedence), the same policy Mova follows with next on and; GDTB's counts of and with and without the implicit then give the base rate Mova's next on and should approach, per genre.

For Mova

36. Discourse relations and coherence §4 (PDTB), §9 (QUD), §11.1, §11.5–11.6. Code: a GDTB scorer next to prag/src/fbench.rs, prag/src/frames.rs (inverted conditionals), world/src/qud.rs. Measures: link precision/recall vs GDTB on GUM dev by genre; QUD answer attachment on hypophora.

Topics: Discourse analysis

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.emnlp-main.684 · arXiv 2411.00491

@inproceedings{liu2024gdtb,
  title = {{GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains}},
  author = {Liu, Yang Janet and Aoyama, Tatsuya and Scivetti, Wesley and Zhu, Yilun and Behzad, Shabnam and Levine, Lauren Elizabeth and Lin, Jessica and Tiwari, Devika and Zeldes, Amir},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  year = {2024},
  doi = {10.18653/v1/2024.emnlp-main.684},
  eprint = {2411.00491},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2024.emnlp-main.684}
}

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