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Dynamic Routing Transformer Network for Multimodal Sarcasm Detection

Summary

The paper tackles multimodal sarcasm detection, where sarcasm in an image-text post is usually signalled by a mismatch between the two modalities. The authors argue that earlier systems capture this mismatch with fixed architectures, so they cannot adapt to the variety of sarcastic pairs. Some posts clash on local details, such as an object versus a phrase. Others clash in overall mood. They propose DynRT-Net, a routing-based dynamic network. Text is encoded with RoBERTa and images with a ViT. A stack of Dynamic Routing Transformer (DynRT) layers follows, and each layer contains a router. The router assigns input-dependent weights to several co-attention variants between text tokens and image patches, and the number of variants grows layer by layer (hierarchical co-attention). TRAR routes only within the image modality, whereas DynRT routes on the cross-modal co-attention itself. On the single public Twitter-based benchmark, the model is reported to beat previous state-of-the-art methods. The work matters because it applies dynamic network design to cross-modal incongruity for the first time.

Key points

  • Architecture: RoBERTa text encoder plus ViT image encoder, followed by stacked DynRT layers and a classifier that combines routed features with image features. Code is available at github.com/TIAN-viola/DynRT.
  • Routing works over at most four types of co-attention mask matrices. Increasing the number of mask types in deeper layers outperformed variants with equal or decreasing types.
  • In ablations, every substitute performed worse than DynRT. The substitutes were TRAR, a standard multimodal transformer layer, plain concatenation of the encoder [CLS] vectors (a sharp drop), and averaged or fixed attention in place of router predictions.
  • Depth varied from 1 to 6 layers; gains peaked early and 4 DynRT layers were chosen as the most stable and best configuration.
  • Limitations: only one public dataset exists, which restricts generalization testing, and only four co-attention designs were explored.

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

Cited by

13 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 12 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.acl-long.139

@inproceedings{tian2023dynamic,
  title = {{Dynamic Routing Transformer Network for Multimodal Sarcasm Detection}},
  author = {Tian, Yuan and Xu, Nan and Zhang, Ruike and Mao, Wenji},
  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.139},
  url = {https://doi.org/10.18653/v1/2023.acl-long.139}
}

References

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