[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/xiong-2025-sarc7-evaluating-sarcasm-detection-generation/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [English](https://md.mova.elamur.ai/free/en/docs/papers/en/) / Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques # Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques **Authors:** Lang Xiong, Raina Gao, Alyssa Jeong · **Year:** 2025 · **Venue:** WiNLP 2025 (Widening NLP Workshop) **Link:** [aclanthology.org/2025.winlp-main.25](https://aclanthology.org/2025.winlp-main.25/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (paper); the utterances come from sitcom dialogue (MUStARD) — not reusable [**0** citations](https://openalex.org/works?filter=cites:W4416033606) openalex · 2026-10-18[Proceedings of the 9th Widening NLP Workshop](https://aclanthology.org/volumes/2025.winlp-main/)[licence](https://creativecommons.org/licenses/by/4.0/) - [Publisher's page](https://aclanthology.org/2025.winlp-main.25/) - [PDF](https://aclanthology.org/2025.winlp-main.25.pdf) - [DOI 10.18653/v1/2025.winlp-main.25](https://doi.org/10.18653/v1/2025.winlp-main.25) - [OpenAlex](https://openalex.org/W4416033606) **Authors (3)** 1. [Lang Xiong](https://openalex.org/A5086192777) [ORCID](https://orcid.org/0009-0004-6100-4096) · h 11 · 421 citations · 39 works 2. Raina Gao 3. Alyssa Jeong Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart. ## What it is about The 690 sarcastic utterances of the MUStARD sitcom dataset are relabelled with seven sarcasm types taken from a classroom-discourse taxonomy: self-deprecating, brooding, deadpan, polite, obnoxious, raging and manic (four annotators, a fifth for agreement: kappa 0.67; brooding, deadpan and polite are hardest even for people). Large models (GPT-4o, Claude 3.5 Sonnet, Gemini 2.5, Qwen 2.5, Llama 4) are prompted to classify the type: chain-of-thought gives the best accuracy (57%), while an «emotion-based» prompt (classify the emotion of the context, then of the utterance, then judge the incongruity) gives the best macro-F1 (0.37) and helps rare types, but makes models «trigger-happy», cutting accuracy on non-sarcastic inputs by about a quarter. For generation, fixing incongruity, shock value, context dependence and emotion per type improves type accuracy by 38% over zero-shot. ## Ideas - I1. The «trigger-happy» effect is a warning for Mova's teacher prompts: asking the teacher explicitly about emotions or incongruity raises false irony labels. Keep the irony question out of the general silver prompt (`prag/src/silver.rs`), or, if irony is asked for, measure the teacher's false-positive rate on a set of known sincere lines (tale lines with sincere narrator framing) before trusting it. - I2. The context-vs-utterance emotion comparison is the same signal as the emotional-shift check proposed in `ducret-2020-linguistic-features-automatic-sarcasm-detection` I1; keep it as one deterministic rule in `prag/src/irony.rs` with a negative control (a joyful reply to good news must not fire). No need for the seven types: with human kappa 0.67 and models at macro-F1 0.37, they are not a target for the SLM. ## For Mova [34. Irony and figurative language](https://md.mova.elamur.ai/free/en/docs/languages/en/34-irony-and-figurative-language/) §2, §9–10. Code: `prag/src/silver.rs` (teacher prompt), `prag/src/irony.rs`. Measures: teacher false-positive rate on sincere lines; precision of the emotional-shift rule with its negative control. ## Cite The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2025.winlp-main.25](https://doi.org/10.18653/v1/2025.winlp-main.25) ``` @inproceedings{xiong2025sarc7, title = {{Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques}}, author = {Xiong, Lang and Gao, Raina and Jeong, Alyssa}, booktitle = {Proceedings of the 9th Widening NLP Workshop}, year = {2025}, doi = {10.18653/v1/2025.winlp-main.25}, url = {https://doi.org/10.18653/v1/2025.winlp-main.25} } ``` - [← previous Incremental Neural Coreference Resolution in Constant Memory](https://md.mova.elamur.ai/free/en/docs/papers/xia-2020-incremental-neural-coreference-resolution-constant/) - [next → CoNLL 2016 Shared Task on Multilingual Shallow Discourse Parsing](https://md.mova.elamur.ai/free/en/docs/papers/xue-2016-conll-2016-shared-task-multilingual/) - [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/) ## References 19 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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