---
title: "Just Like a Human Would, Direct Access to Sarcasm — Min 2023"
description: "Sarcasm in social media posts usually pairs positive wording with an unstated negative situation."
authors: ["Changrong Min", "Ximing Li", "Liang Yang", "Zhilin Wang", "Bo Xu", "Hongfei Lin"]
year: 2023
venue: "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2023.acl-long.566/"
doi: "10.18653/v1/2023.acl-long.566"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/min-2023-just-like-human-would-direct/"
---
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# Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction

**Authors:** Changrong Min, Ximing Li, Liang Yang, Zhilin Wang, [Bo Xu](https://md.research.elamur.ai/free/authors/bo-xu/), [Hongfei Lin](https://md.research.elamur.ai/free/authors/hongfei-lin/) · **Year:** 2023 · **Venue:** Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.acl-long.566/) · [PDF](https://aclanthology.org/2023.acl-long.566.pdf) · [DOI 10.18653/v1/2023.acl-long.566](https://doi.org/10.18653/v1/2023.acl-long.566) · [OpenAlex](https://openalex.org/W4385571519)

**Citations:** 12 citations (openalex-snapshot · 2026-10-14) · 4 in our library, of the 140673 papers whose bibliographies we read

## Summary
Sarcasm in social media posts usually pairs positive wording with an unstated negative situation. Detectors that look only at the visible text, or that inject external knowledge in abstract form, often miss that hidden context. The authors draw on the psycholinguistic "direct access" view, which holds that humans grasp the literal meaning and the implied situation together. They propose SD-APRR, which treats each post as an incomplete text. For every sample, the commonsense reasoner COMET generates a likely outcome ([xEffect]) and an emotional reaction ([xReact]). These are appended to the original sentence as plain-language clauses, producing an "event-augmented" sample.

The augmented text is embedded with GloVe and a Bi-LSTM, then parsed into a dependency graph. A graph neural encoder processes the graph, with a dynamic-masking denoiser that down-weights irrelevant or wrong COMET output. A single-layer MLP makes the final decision. On four benchmark datasets, SD-APRR beats strong baselines, including SarDeCK and ADGCN. This suggests that spelling out implied events as text works better than fusing knowledge at the embedding level.

## Key points
- Evaluation covers four sarcasm benchmarks: SemEval18, IAC-V2, Ghosh and iSarcasm. Most of these come from social media.
- COMET (trained on ATOMIC 2020, 23 relations) supplies the result and reaction. They are linked to the post with template phrases of the form "…, then may [result] and I feel [reaction]".
- In ablations, removing either the results or the reactions lowers performance on all datasets. Removing the masking module hurts on IAC-V2 but is less reliable on short texts like Ghosh. A BERT-encoder variant also gains from both augmentations, tested on Ghosh and IAC-V2.
- Encoder depth matters. One layer works best for SemEval18, and three layers work best elsewhere, so L = 3 is the recommended default.
- Visualizing the mask weights shows that the denoiser suppresses mismatched COMET outputs (e.g., a wrong "happy" reaction) and stop words.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cite

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

```bibtex
@inproceedings{min2023just,
  title = {{Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction}},
  author = {Min, Changrong and Li, Ximing and Yang, Liang and Wang, Zhilin and Xu, Bo and Lin, Hongfei},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2023},
  doi = {10.18653/v1/2023.acl-long.566},
  url = {https://doi.org/10.18653/v1/2023.acl-long.566}
}
```

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