---
title: "Multimodal Sarcasm Target Identification in — Wang 2022"
description: "Sarcasm Target Identification (STI) asks what or whom a sarcastic post is mocking."
authors: ["Jiquan Wang", "Lin Sun", "Yi Liu", "Meizhi Shao", "Zengwei Zheng"]
year: 2022
venue: "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2022.acl-long.562/"
doi: "10.18653/v1/2022.acl-long.562"
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/wang-2022-multimodal-sarcasm-target-identification-tweets/"
---
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# Multimodal Sarcasm Target Identification in Tweets

**Authors:** Jiquan Wang, [Lin Sun](https://md.research.elamur.ai/free/authors/lin-sun/), [Yi Liu](https://md.research.elamur.ai/free/authors/yi-liu/), Meizhi Shao, Zengwei Zheng · **Year:** 2022 · **Venue:** Proceedings of the 60th 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/2022.acl-long.562/) · [PDF](https://aclanthology.org/2022.acl-long.562.pdf) · [DOI 10.18653/v1/2022.acl-long.562](https://doi.org/10.18653/v1/2022.acl-long.562) · [OpenAlex](https://openalex.org/W4285247267)

**Citations:** 21 citations (openalex-snapshot · 2026-10-14) · [7](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Sarcasm Target Identification (STI) asks what or whom a sarcastic post is mocking. Text-only methods struggle when a tweet gives little context or never names the target. In the ALTA shared task, such 'OUTSIDE' cases make up over 30% of the data. The authors define a new task, Multimodal Sarcasm Target Identification (MSTI). It has two parts: tagging target phrases in the tweet text (TSTI) and drawing bounding boxes around target objects in the attached image (VSTI). They build an MSTI dataset and propose an end-to-end model that solves both parts jointly:

- **Text:** BERT embeddings.
- **Image:** multi-scale features from CSPDarkNet53.
- **Fusion:** convolutional converters shrink the visual features into visual tokens, and a Transformer-based Multi-scale Cross-modality Encoder fuses them with the text. Transposed convolutions then restore the visual maps for a YOLO-style detector.
- **Output:** a BiLSTM-CRF tags the text.

Adding images improves textual target tagging by roughly 5 F1 points, and visual targets are detected at 51.9% AP50. This shows that images are a useful signal for analysing sarcasm on social media.

## Key points
- **Architecture:** the model has five parts. B2M is a converter that reduces each of the three backbone scales to 5×5 grids of 25 visual tokens. MCE is the cross-modality Transformer encoder. M2N rebuilds the visual maps using transposed convolutions. TSTI is handled by a BiLSTM-CRF, and VSTI by an SPP/PANet neck with a detection head.
- **Best configuration:** CSPDarkNet53 with BERT-Large reaches, on the test set, 37.2 EM, 47.9 F1, 32.6 AP, 51.9 AP50 and 34.6 AP75. CSPDarkNet53 beats the ResNet151 and VGG19 backbones.
- **Comparison with text-only models:** on average, the model outperforms BERT-based textual sequence taggers by 5.3% F1 and 4.4% EM accuracy. Removing the image costs 5.1 F1 and 4.1 EM.
- **Baseline comparison:** Mask R-CNN and YOLOv4 trained directly on sarcastic objects score about 2% higher AP than pretrained multimodal baselines that rely on generic RoIs.
- **Attention analysis:** visualising text-to-image and image-to-text attention shows the model focusing on target regions in the image and on key words in the text. Ablations suggest that text carries more explicit sarcasm information than images.

*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/)

## Cited by

Cited by

7 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 6 not yet.

- [CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models](https://md.research.elamur.ai/free/papers/chen-2024-cofipara-coarse-fine-paradigm-multimodal/)

## Cite

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

```bibtex
@inproceedings{wang2022multimodal,
  title = {{Multimodal Sarcasm Target Identification in Tweets}},
  author = {Wang, Jiquan and Sun, Lin and Liu, Yi and Shao, Meizhi and Zheng, Zengwei},
  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2022},
  doi = {10.18653/v1/2022.acl-long.562},
  url = {https://doi.org/10.18653/v1/2022.acl-long.562}
}
```

- [← previous Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network](https://md.research.elamur.ai/free/papers/liang-2022-multi-modal-sarcasm-detection-cross/)
- [next → MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation](https://md.research.elamur.ai/free/papers/ye-2022-multiwoz-2-4-multi-domain/)
- [↑ back to papers of 2022](https://md.research.elamur.ai/free/papers/2022/page/2/)

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