---
title: "When Do “More Contexts” Help with Sarcasm Recognition? (2024)"
description: "The authors build a single framework with four context sources: Word2Vec… 72.1 accuracy on IAC-V1. 22 references, cited by 1 paper here."
authors: ["Ojas Nimase", "Sanghyun Hong"]
year: 2024
venue: "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)"
source: "https://aclanthology.org/2024.lrec-main.1525/"
arxiv: "2403.12469"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/nimase-2024-when-do-more-contexts-help/"
---
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# When Do “More Contexts” Help with Sarcasm Recognition?

**Authors:** Ojas Nimase and Sanghyun Hong · **Year:** 2024 · **Venue:** [Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)](https://md.research.elamur.ai/free/en/venues/lrec/) ([LREC 2024](https://md.research.elamur.ai/free/en/venues/lrec/2024/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.lrec-main.1525/) · [PDF](https://aclanthology.org/2024.lrec-main.1525.pdf) · [arXiv 2403.12469](https://arxiv.org/abs/2403.12469) · [OpenAlex](https://openalex.org/W7131816043)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks how much sarcasm recognition actually gains from giving a model extra context, and which kinds of context matter most. Earlier work added cues such as word sentiment contrast or richer sentence encoders one at a time, but nobody had compared them side by side or stacked them. The authors build a single framework with four context sources: Word2Vec word vectors (A1), fine-tuned RoBERTa and BERTweet sentence embeddings (A2), BERTweet re-trained with a SimCLR-style contrastive loss on SarcasmSIGN sarcastic/literal pairs (A3), and a concatenation of all of them (A4). Each source feeds a small feedforward classifier. They test on IAC-V1, IAC-V2 and Tweets.

The plain concatenation matches dedicated baselines (Liu et al., 2022) on the IAC sets and beats them on Tweets. This suggests that some earlier gains may come from extra data rather than clever architecture. A sample-by-sample review then shows a cost: several items fixed only by the full model seem to be classified correctly because the model has absorbed attitudes toward particular people or topics. Pushing accuracy further may therefore mean learning societal bias.

## Key points
- Data: IAC-V1, IAC-V2 and Tweets (SemEval-2018 irony data), all with binary sarcastic/non-sarcastic labels. SarcasmSIGN, after removing duplicates, supplies anchor, positive and negative triples for contrastive training.
- The A4 concatenated model reaches about 72.1 accuracy on IAC-V1, 84.0 on IAC-V2 and 82.0 on Tweets. The baseline RoBERTa reaches roughly 72–73 on Tweets.
- Word-level embeddings alone (A1) perform near or below chance, with recall of 100, which points to a degenerate classifier. Sentence-level encoders help a lot, and tweet-pretrained BERTweet helps most on Tweets.
- The SimCLR contrastive re-training (temperature 0.7, 256-d projection) adds almost nothing over plain BERTweet.
- The concatenated input is 39,936-dimensional, projected down to 768. The A4 model is trained with an F-β loss. Code is public on GitHub (secure-ai-systems-group/sarcasm-detection).

## Interesting ideas
- The contrastive triples pair a literal sentence with an unrelated literal sentence as the positive and with its own sarcastic rewording as the negative, which isolates what irony changes while topic stays fixed.
- Instead of trusting aggregate accuracy, the authors inspect exactly the items that a richer model fixes over the previous one, to see why each gain happened.
- A recall of 100 paired with chance-level accuracy reveals a classifier that always outputs one label, which is easy to miss if only one metric is reported.
- Some correct predictions turned out to depend on hostility toward named entities (a reality-show contestant, politicians, a singer) rather than on linguistic cues. This shows that accuracy can reward bias.
- Simply stacking diverse representations matched specialised architectures, which suggests that prior gains came from extra information rather than design.

## For Mova

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

- **I1** (frames): SarcasmSIGN-style literal/sarcastic pairs could serve as a minimal-pair test set for the irony module in prag, checking that the same frames get flipped evaluative polarity.
- **I2** (learn): diffing per-sample wins between parser or reader versions, and labelling the cause of each, would show whether a "repair" reflects real understanding or a shortcut.
- **I3** (learn): add an always-majority baseline as a standing negative control in evaluation reports, so that no component is credited when it simply matches that baseline.
- **I4** (frames): irony decisions should be explained through explicit cues such as evaluative contrast or exaggeration, and verdicts that rest only on the identity of an entity in the world model should be flagged.
- **I5** (learn): when a new witness parser or teacher tier improves scores, compare it against a plain combination of existing witnesses before attributing the gain to its method.

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

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## Cite

Cite as: Ojas Nimase and Sanghyun Hong. 2024. When Do “More Contexts” Help with Sarcasm Recognition?. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).

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

```bibtex
@inproceedings{nimase2024when,
  title = {{When Do “More Contexts” Help with Sarcasm Recognition?}},
  author = {Ojas Nimase and Sanghyun Hong},
  booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  year = {2024},
  doi = {10.63317/2tbwzw8bki8f},
  eprint = {2403.12469},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.63317/2tbwzw8bki8f}
}
```

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## References

22 references, 18 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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8. Yiyi Liu, Yequan Wang, Aixin Sun, Xuying Meng, Jing Li, and Jiafeng Guo. 2022. [A dual-channel framework for sarcasm recognition by detecting sentiment conflict](https://md.research.elamur.ai/free/en/papers/liu-2022-dual-channel-framework-sarcasm-recognition/). In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1670–1680, Seattle, United States. Association for Computational Linguistics.
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17. Angela Persicke, Jonathan Tarbox, Jennifer Ran-ick, and Megan St. Clair. 2013. Teaching children with autism to detect and respond to sarcasm. Research in Autism Spectrum Disorders, 7(1):193–198. [↗](https://doi.org/10.1016/j.rasd.2012.08.005)
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*Updated 1 November 2026*