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A Formalism and an Algorithm for Computing Pragmatic Inferences and Detecting Infelicities

Paper · 1995 · Daniel Marcu

Summary

Linguists use "infelicity" freely, yet no one had given it a formal or computational definition. Marcu's 1994 University of Toronto MSc thesis supplies one for a specific case: the oddness that arises when a pragmatic inference is cancelled. He starts from a known contrast. Implicatures and presuppositions are never stated outright, so they can be defeated, while most semantic inferences cannot. He then splits the defeasible part in two: inferences that may be cancelled without strain, and inferences whose cancellation sounds wrong.

To hold these three strengths of information he builds stratified logic, a propositional and first-order system with:

  • a lattice-based semantics,
  • semantic tableaux,
  • an ordering of models that prefers "optimistic" interpretations.

Within it he formalizes "u presupposes p" and "u is infelicitous". He uses it to address the presupposition projection problem and puzzles about nonexistent objects. A Lisp program takes stratified knowledge plus the logical form of utterances and returns felicitous optimistic models, presuppositions and infelicity warnings.

Key points

  • Information is graded into three levels: undefeasible, felicitously defeasible and infelicitously defeasible. Pragmatic infelicity is defined as defeating the third kind.
  • Stratified logic has propositional and first-order versions, Beth-style tableaux for building models, and a model ordering. Its default-reasoning behaviour is shown on the classic Tweety/penguin example.
  • Projection through disjunctions and conditionals is handled with and without cancellation. Examples include the disjunction "Chris is a bachelor or a spinster" and a conditional stating that, should Mary have attended the party, John will regret Sue's coming.
  • Existential presuppositions come from definite reference. The test case is "Ross worships Zeus", run both with no knowledge about Zeus and with full knowledge. The approach is compared with Parsons's and Hobbs's theories of nonexistence.
  • The Lisp implementation runs in stages:
    1. find model schemata,
    2. keep the felicitous ones,
    3. select the optimistic ones,
    4. collect presuppositions.
  • One example shows the contrast directly. "John does not regret that Mary came… because she didn't come" cancels a presupposition felicitously, while "John regrets that Mary came…, but she didn't come" is flagged as infelicitous.

Interesting ideas

  • I1. Mark every lexical inference with its strength (hard, cancellable, or cancellable-but-odd) instead of a single default flag, so a contradiction can be labelled either as a legitimate retraction or as a defect.
  • I2. Lexical knowledge is split into a semantic part, stated as plain entailments, and a pragmatic part, stated as rules that fire only when a word is actually uttered, so a word's meaning and its usage effects stay separate.
  • I3. Interpretation is done by building all models with tableaux and then preferring the "optimistic" ones that keep the most defeasible inferences, so presuppositions emerge as what survives in every preferred model.
  • I4. Existence is a defeasible predicate triggered by definite reference rather than built into quantification, so a text can speak of Zeus without committing the world to Zeus existing.
  • I5. The thesis's test set is built from minimal pairs: the same presupposition trigger, once cancelled acceptably and once cancelled oddly.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (frames): implicatures and presuppositions in prag (from "regret", "realize", clefts, "not a bachelor") could carry this three-way tag, so that a cancelled felicitous inference is dropped silently while a cancelled infelicitous one raises a visible, explainable warning.
  • I2 (world): compiled verb and noun knowledge (e.g., "bachelor" entails male/adult/unmarried) could keep entailments in the ontology and attach utterance-triggered pragmatic rules separately, so the world builder knows which facts it may retract.
  • I3 (world): when a text admits several world states, Mova could keep the candidates, rank them by how many cancellable inferences they keep, and report presuppositions as facts common to all top-ranked worlds, with the ranking as the explanation.
  • I4 (world): entities from fables and tales could get a separate, cancellable "exists in the real world" flag, letting the answer layer distinguish in-story facts from real-world claims.
  • I5 (learn): pragmatic evaluation could use such pairs as negative controls, checking that the system flags only the infelicitous member and never the acceptable cancellation.

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

 Institutions

🎓University of TorontoToronto · Canada96 works · 142,007 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as Daniel Marcu. 1995. A Formalism and an Algorithm for Computing Pragmatic Inferences and Detecting Infelicities. arXiv:cmp-lg/9504019.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.cmp-lg/9504019 · arXiv cmp-lg/9504019

@misc{marcu1995formalism,
  title = {{A Formalism and an Algorithm for Computing Pragmatic Inferences and Detecting Infelicities}},
  author = {Daniel Marcu},
  year = {1995},
  doi = {10.48550/arxiv.cmp-lg/9504019},
  eprint = {cmp-lg/9504019},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.cmp-lg/9504019}
}

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