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QUD-Based Annotation of Discourse Structure and Information Structure: Tool and Evaluation

What it is about

The paper tests the QUD-tree method of Riester and colleagues: a text is cut into atomic assertions (sentences, plus splits at coordinations and before optional adjuncts), each assertion is the answer to one explicit or implicit question under discussion, and the questions form the inner nodes of a tree whose leaves are the assertions in text order. Implicit QUDs are constrained by three principles: the QUD must be answerable by the assertion it dominates (congruence), may contain only given or salient material (givenness), and should contain as much given material as possible (maximal anaphoricity); parallel assertions share a QUD whose background is their common material. From the QUD, information structure follows: focus = the part answering the QUD, background = material in the QUD, contrastive topic, and non-at-issue material (e.g. «It was clear from the President's speech that…»). Two pairs of annotators worked on an English and a German interview with a new tool (TreeAnno): agreement on tree structure is moderate (kappa .45–.53, computed on the matrix of all segment spans after Marcu et al.), on information-structure labels substantial (.67–.69 overall; focus .63–.67).

Ideas

For Mova

languages/en/29-ellipsis-word-order-information-structure.md (focus, background, contrastive topic) and 36-discourse-relations.md §9 and §11.5 (QUD stack). Code: world/src/qud.rs, prag/src/frames.rs, world/src/knowledge.rs. Measures: span-matrix kappa against teacher trees; course answers after focus-aware scoring.

Library summary

The Research library's own summary of the paper, in our words — not the paper's text.

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/L18-1304.pdf

Summary

The paper tests whether the QUD-tree framework can be annotated reliably on real data. The framework treats each assertion in a text as the answer to an implicit or explicit Question under Discussion (QUD). Reconstructing these hidden questions yields a hierarchical discourse tree: assertions are leaves, questions are internal nodes. The same questions also fix the information-structure labels within each assertion. Annotators derive the QUDs from segmented text using principles such as question–answer congruence, givenness, maximal anaphoricity and parallelism.

The authors present TreeAnno, a web-based tool built for this kind of annotation. Four trained annotators, working in pairs, used it on two interviews: an English one with Edward Snowden and a German radio interview from the GRAIN corpus. For discourse structure, agreement was measured with a κ statistic computed over a span-based chart, following Marcu et al. (1999). Agreement was reasonable for discourse structure and good for information structure. The work matters because it is presented as the first corpus framework that actually reconstructs QUDs, combining discourse and information structure in one annotation.

Key points

  • The information-structure labels are focus (F), background (BG), contrastive topic (CT) and non-at-issue material (NAI). Aboutness topics were left out to avoid nested categories.
  • The English data are two sections of the ARD Snowden interview (Jan. 2014), with 60 and 69 segments. The German data are an SWR2 interview with Thomas Oppermann (Sept. 2015), with 158 segments, taken from the GRAIN corpus.
  • TreeAnno stores annotations in a UIMA document and uses LanguageTool for initial sentence and token splitting. Annotators build trees by indenting segments, much like an outliner.
  • TreeAnno exports to tree-oriented XML, bracket notation, GraphViz drawings or a chart matrix. It also lets users compare annotations and is released as open source.
  • QUD trees combine Büring's d-trees with a mapping to SDRT discourse graphs. They contain question nodes where SDRT would have discourse relations.

Topics: Discourse analysis

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.63317/368a5yfgwg9q

@inproceedings{kuthy2018qud,
  title = {{QUD-Based Annotation of Discourse Structure and Information Structure: Tool and Evaluation}},
  author = {Kuthy, Kordula De and Reiter, Nils and Riester, Arndt},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  doi = {10.63317/368a5yfgwg9q},
  url = {https://doi.org/10.63317/368a5yfgwg9q}
}

References

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