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On the Information Conveyed by Discourse Markers

Summary

The paper asks how much a discourse connective tells a reader about the relation between two text spans. It frames this as a first step towards a surprisal-style model of discourse processing grounded in the Uniform Information Density (UID) hypothesis. Using only explicit relations in the Penn Discourse Treebank (PDTB), the authors run three analyses.

First, they define a markedness score based on normalized pointwise mutual information between relations and connectives. They use it to test whether relations that readers expect by default are signalled more ambiguously. Second, they sort out distinct kinds of ambiguity: connectives with several senses, and cases where two relations hold at once. Third, they propose an "enhancement" measure that splits a connective's information gain across the levels of the PDTB sense hierarchy.

The results fit the continuity and causality-by-default hypotheses: hard-to-predict contrastive relations get unambiguous markers, while expected ones tolerate vague markers. The work matters for psycholinguistic modelling and for automatic discourse parsing. It also raises the question of whether PDTB sense distinctions match what humans actually infer.

Key points

  • Data: explicit PDTB relations, with double-labelled connectives counted once per sense, giving 19,458 relation instances. Level-1 counts are Temporal 3,696, Contingency 3,741, Expansion 6,431 and Comparison 5,590.
  • Markedness is computed by rescaling base-2 npmi to [0, 1] and weighting it by p(connective | relation).
  • COMPARISON relations show the highest markedness and CONTINGENCY lower markedness. TEMPORAL relations are surprisingly low because their connectives also often mark CONTINGENCY.
  • Ambiguity patterns differ by connective:
    • Polysemous connectives (while, since, when) add a little information at every hierarchy level.
    • Weak connectives (and, but, if) say almost nothing about subtypes.
    • Meanwhile, which marks inherently co-occurring relations, gives low gain everywhere.
  • Summed over the three levels, the enhancement scores equal the total relative entropy between the relation distributions before and after the connective. Adversatives mainly help at level 1, while temporally directional connectives help most at deeper levels.

Our summary of the paper, in our own words β€” not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Discourse analysis

Cited by

14 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 10 not yet.

Cite

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

@inproceedings{asr2013information,
  title = {{On the Information Conveyed by Discourse Markers}},
  author = {Asr, Fatemeh Torabi and Demberg, Vera},
  booktitle = {Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)},
  year = {2013},
  url = {https://aclanthology.org/W13-2610/}
}

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