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# A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives

**Authors:** Qingcheng Zeng, An-Ran Li · **Year:** 2022 · **Venue:** COLING 2022 **Link:** [aclanthology.org/2022.coling-1.69](https://aclanthology.org/2022.coling-1.69/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**4** citations](https://openalex.org/works?filter=cites:W4295691254) openalex · 2026-10-18[**10** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zeng-2022-survey-automatic-irony-processing-linguistic/) of the 140673 papers whose bibliographies we read[Proceedings of the 29th International Conference on Computational Linguistics](https://aclanthology.org/volumes/2022.coling-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2022.coling-1.69/)
- [PDF](https://aclanthology.org/2022.coling-1.69.pdf)
- [arXiv 2209.04712](https://arxiv.org/abs/2209.04712)
- [OpenAlex](https://openalex.org/W4295691254)

**Authors (2)**

1. [Qingcheng Zeng](https://openalex.org/A5103169944) [ORCID](https://orcid.org/0009-0001-5196-0939) · h 6 · 158 citations · 25 works
2. [An-Ran Li](https://openalex.org/A5000173131) [ORCID](https://orcid.org/0000-0002-3592-4153) · h 17 · 1142 citations · 73 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

A survey of irony research. It reviews definitions (saying the opposite vs echoic and relevance-based accounts), situational vs verbal irony, the verbal, grammatical and construction-level markers of irony (core evaluative words such as *thank*, *congratulate*, *welcome*, *interesting*; patterns like «NP would love…»; idiomatic ironic constructions that trigger reversal without much context), the functions of irony (politeness, softened criticism, humour) and the irony–sarcasm border (sarcasm as the hurtful, face-threatening kind, though most computational work does not separate them). It tabulates datasets (mostly Twitter, built by hashtags and keywords, mostly binary labels; only one fine-grained scheme) and systems from rule-based pattern matchers through feature-based classifiers, recurrent networks and fine-tuned transformers, and calls for datasets from literature, conversation and news, a shared annotation scheme, and work on irony generation and on irony's effect on sentiment analysis.

## Ideas

- I1. Add idiomatic ironic constructions to Mova's expression base as fixed expressions with their meaning (*a likely story*, *big deal*, *tell me about it*, *that's just great*, *thanks a lot*, *you don't say*, *what a surprise*), so that the expression lens reads them by meaning before the irony rule runs. Pattern constructions of the «NP would love to…» type go into `prag/src/irony.rs` as cues with measured precision (see `oraby-2016-creating-characterizing-diverse-corpus-sarcasm` I1).
- I2. Separate sarcasm from irony by the face-threat criterion the survey reports: an ironic evaluation aimed at the addressee or a present person (second-person target, vocative) is sarcasm and also an impoliteness cue for the politeness detectors in `world/src/talk.rs`; irony about a situation is not. A one-line rule over the candidate's target.
- I3. The survey's call for literary data fits Mova: irony in tales (fables especially) is a test genre missing from the field. Collect the tale lines where the teacher's `means` reverses the literal evaluation as a small public-domain irony set with their explanations, usable by the irony chapter as examples.

## For Mova

[34. Irony and figurative language](https://md.mova.elamur.ai/free/en/docs/languages/en/34-irony-and-figurative-language/) §1–4 (definitions, theories, cues), §8 (idioms and the expression lens), §10, and `32-politeness-and-hedging.md` §5 (impoliteness). Code: `prag/src/irony.rs`, the expression base ([Expressions with their real meanings — the pragmatics lens](https://md.mova.elamur.ai/free/en/docs/docs/pragmatics-expressions/)), `world/src/talk.rs`. Measures: irony-candidate precision on silver; count of ironic constructions read by meaning.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [arXiv 2209.04712](https://arxiv.org/abs/2209.04712)

```
@inproceedings{zeng2022survey,
  title = {{A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives}},
  author = {Zeng, Qingcheng and Li, An-Ran},
  booktitle = {Proceedings of the 29th International Conference on Computational Linguistics},
  year = {2022},
  eprint = {2209.04712},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2209.04712}
}
```

- [← previous eRST: A Signaled Graph Theory of Discourse Relations and Organization](https://md.mova.elamur.ai/free/en/docs/papers/zeldes-2025-erst-signaled-graph-theory-discourse/)
- [next → Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders](https://md.mova.elamur.ai/free/en/docs/papers/zhang-2024-can-your-model-tell-negation/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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