Interpreting Verbal Irony: Linguistic Strategies and the Connection to the Type of Semantic Incongruity

What it is about

Instead of detecting irony, the paper studies how hearers spell out what an ironic message means. Five crowd workers rewrote each of 1,000 ironic tweets as its intended meaning (4,762 valid pairs). From 500 pairs a linguist derived a typology of interpretation strategies: lexical antonyms (great → terrible, including contextual opposites like passionate → boring) and antonym phrases; simple negation of the main predicate; weakening of sentiment (love → don't like; dropping an intensifier: so healthy → not healthy); turning a rhetorical question into a statement; a counterfactual wish for ironic thanks or gladness about a past event (glad you told me → I wish you hadn't); and free pragmatic inference. Rule-based detectors (antonym lexicons, word alignment, dependency trees, negation markers, intensifier lists) find the main strategies at about 90% F1 on two test sets. Experts marked 38.7% of the messages as explicit incongruity (positive word and negative situation both in the text) and 61.3% as implicit; antonyms are used more for explicit cases, negation more for implicit ones, and irony markers appear twice as often with implicit incongruity.

Ideas

• I1. Give prag/src/irony.rs a deterministic interpretation step after detection, built from the paper's strategies, so that an irony candidate also gets a means line with its reason: replace a strong evaluative word by its antonym from the polarity lexicon (lovely → unpleasant); otherwise negate the main evaluative predicate and drop intensifiers (so kind → not kind); a rhetorical question becomes the opposite statement; ironic thank you / glad about a narrated bad event becomes «the speaker wishes it had not happened». Measure agreement with the teacher's means on the silver sentences marked ironic (the lion's «singing» example in prag — Mova pragmatics: LLM silver and a transparent SLM is one).
• I2. Pair the antonym list with the polarity lexicon in prag/src/irony.rs (~65 positive, ~70 negative lemmas): each strong positive word gets its opposite, written by hand from public-domain sources (WordNet antonyms are open). This is the resource the interpretation step needs and is small.
• I3. Use the explicit/implicit split for evaluation: report irony-candidate precision separately for explicit incongruity (both sides in the sentence) and implicit (the negative side is in the earlier frames of the story). Implicit cases need the story context, which Mova's frames have and tweet models do not; a candidate rule that looks for the negative situation in the previous frames of the same entity is the natural extension.

For Mova

34. Irony and figurative language §3–4 (theories and cues of irony), §6 (rhetorical questions), §10 (Mova's irony detector). Code: prag/src/irony.rs (interpretation step, antonyms), prag/src/frames.rs (story context for implicit incongruity). Measures: agreement with silver means on irony lines; precision split by explicit/implicit.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.7275/91ey-3n44

@inproceedings{ghosh2020interpreting,
  title = {{Interpreting Verbal Irony: Linguistic Strategies and the Connection to the Type of Semantic Incongruity}},
  author = {Ghosh, Debanjan and Musi, Elena and Muresan, Smaranda},
  booktitle = {Proceedings of the Society for Computation in Linguistics 2020},
  year = {2020},
  doi = {10.7275/91ey-3n44},
  url = {https://doi.org/10.7275/91ey-3n44}
}

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