---
title: "X-PuDu at SemEval-2022 Task 6 — Han 2022"
description: "The paper presents X-PuDu, a system for SemEval-2022 Task 6 (iSarcasmEval), which detects intended sarcasm in English and Arabic tweets."
authors: ["Yaqian Han", "Yekun Chai", "Shuohuan Wang", "Yu Sun", "Hongyi Huang", "Guanghao Chen", "Yitong Xu", "Yang Yang"]
year: 2022
venue: "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)"
source: "https://aclanthology.org/2022.semeval-1.140/"
doi: "10.18653/v1/2022.semeval-1.140"
arxiv: "2211.16883"
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/en/papers/han-2022-x-pudu-semeval-2022-task/"
---
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# X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection

**Authors:** Yaqian Han, Yekun Chai, [Shuohuan Wang](https://md.research.elamur.ai/free/en/authors/shuohuan-wang/), [Yu Sun](https://md.research.elamur.ai/free/en/authors/yu-sun/), Hongyi Huang, Guanghao Chen, Yitong Xu, Yang Yang · **Year:** 2022 · **Venue:** Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.semeval-1.140/) · [PDF](https://aclanthology.org/2022.semeval-1.140.pdf) · [DOI 10.18653/v1/2022.semeval-1.140](https://doi.org/10.18653/v1/2022.semeval-1.140) · [arXiv 2211.16883](https://arxiv.org/abs/2211.16883) · [OpenAlex](https://openalex.org/W4287888692)

**Citations:** 10 citations (openalex-snapshot · 2026-10-14) · [5](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper presents X-PuDu, a system for SemEval-2022 Task 6 (iSarcasmEval), which detects intended sarcasm in English and Arabic tweets. The task data were labelled by the tweet authors, who also wrote non-sarcastic rephrasings of their sarcastic posts. There are three subtasks:

- **Task A:** binary detection on a single text.
- **Task B:** English-only multi-label tagging into six ironic-speech categories.
- **Task C:** picking the sarcastic member of a sarcastic/rephrase pair.

The team fine-tuned pre-trained Transformers (ERNIE-M for both languages, DeBERTa for English) with a classification head on the [CLS] token. In Task C, both texts were joined with [SEP] and fed to a cross-encoder. For Tasks A and C, they also tried multilingual learning, merging English and Arabic data into one fine-tuning run of ERNIE-M. Results came from ensembles averaged over 10-fold cross-validation and several hyperparameter settings.

The system placed 2nd/43 (Task A English), 5th/22 (Task B) and 1st/16 (Task C English). Joint bilingual training clearly helped on the paired task but not on single-sentence detection. This suggests that a contrastive pair exposes the ironic signal more readily.

## Key points
- The training set has 3468 texts, of which 867 are sarcastic and 2601 are not. Task B labels are very skewed: sarcasm 713, irony 155, rhetorical_question 101, overstatement 40, satire 25, understatement 10.
- The official metrics differ by subtask: Task A uses F1 on the sarcastic class, Task B uses macro-F1, and Task C uses accuracy.
- Submitted scores were:
  - Task A: DeBERTa reached 56.91 F1 in English and ERNIE-M reached 41.87 in Arabic.
  - Task B: DeBERTa scored 7.99, as reported.
  - Task C: DeBERTa reached 87.00 accuracy in English and monolingual ERNIE-M reached 84.00 in Arabic.
- Effect of multilingual ERNIE-M:
  - Task C rose to 90.50 in Arabic (from 84.00) and to 82.50 in English (from 75.00). The Arabic score would have ranked about 2nd, but it was not submitted in time.
  - Task A stayed roughly level with the monolingual setting (40.36 vs 41.87 in Arabic).
- Training setup:
  - Optimiser: AdamW with weight decay 0.01 and 10% warmup.
  - Learning rates: peak values of 1e-5 and 5e-6.
  - Regularisation: dropout 0.15.
  - Batching: effective batch 64 via gradient accumulation on 8 V100 GPUs.
  - Ensembling: averaged over 10 folds × learning rates × 20/30 epochs.
  - External data: adding Kaggle data with "figurative" and "irony" folded into "sarcasm" brought no gain.

## Interesting ideas
- Deciding which of two near-paraphrases is ironic proved much easier than judging one sentence alone, because the contrast isolates the ironic cue. — For Mova (frames): the irony detector in prag could compare a sentence's frames with those of a literal paraphrase, flagging irony where the two frames diverge in polarity or intensity.
- The dataset pairs each sarcastic post with its author's literal restatement, giving a direct record of the intended meaning. — For Mova (frames): such pairs could serve as gold targets for implicature, testing whether the derived "what was really meant" matches the author's rephrase.
- Merging loosely related labels from an outside corpus added annotator noise rather than signal, even when training data were scarce. — For Mova (learn): before folding external or LLM-tier labels into silver data, check their category mapping against a held-out control instead of assuming more data helps.
- With little data, the authors used k-fold cross-validation for model choice and averaged all fold models for the final prediction, rather than trusting one random split. — For Mova (learn): evaluations on small school-test or fable QA sets could report fold-averaged scores to avoid mistaking split luck for real repair-rule gains.
- Training one model on the same objective in two languages helped where the task structure was strong (pairs) but not elsewhere. — For Mova (back): when translating through the meta-world, aligned bilingual pairs might be most useful for contrastive, structured checks rather than for single-sentence classifiers.

*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/en/shelves/linguistics/)

## Cited by

Cited by

5 papers of our library cite it (of the 140884 whose bibliographies we read); 2 of them have a page here, 3 not yet.

- [Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/farha-2022-sarcasm-detection-is-way-too/)
- [SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic](https://md.research.elamur.ai/free/en/papers/farha-2022-semeval-2022-task-6-isarcasmeval/)

## Cite

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

```bibtex
@inproceedings{han2022x,
  title = {{X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection}},
  author = {Han, Yaqian and Chai, Yekun and Wang, Shuohuan and Sun, Yu and Huang, Hongyi and Chen, Guanghao and Xu, Yitong and Yang, Yang},
  booktitle = {Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)},
  year = {2022},
  doi = {10.18653/v1/2022.semeval-1.140},
  eprint = {2211.16883},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2022.semeval-1.140}
}
```

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## References

23 references, 14 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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