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Exploiting Sentiment and Common Sense for Zero-shot Stance Detection

Summary

Zero-shot stance detection asks a model to decide whether a text supports, opposes, or is neutral toward a topic it never saw in training. The topic is often only hinted at in the document. Luo et al. argue that two kinds of general knowledge can help: commonsense links between concepts, and the sentiment expressed in the text.

Their system, evaluated on the VAST benchmark, has two parts:

  • Commonsense module. A graph autoencoder is pre-trained separately on a ConceptNet sub-graph built around VAST vocabulary. It uses stacked RGCN encoders and a DistMult decoder, trained to recover missing edges with negative sampling, and yields concept representations.
  • Stance classifier. BERT is fused with SentiBERT sentiment features through cross attention, and the graph features are added before classification.

The full model (BS-RGCN) outperforms the previous best methods, including CKE-Net, in both zero-shot and few-shot settings. Ablations confirm that each component contributes. The study matters because it shows that reusable external knowledge can make up for the lack of labelled data on new topics. The code is public in the StanceCS GitHub repository.

Key points

  • Graph construction: seed words are the nouns, adjectives and adverbs from VAST training texts. Keeping one-hop neighbours of these seeds reduces ConceptNet (about 14M edges) to a sub-graph of roughly 310k concepts and 750k edges. The most frequent relations include RelatedTo, IsA and Synonym.
  • Sentiment alone helps: a variant that classifies from the SentiBERT-fused [CLS] representation reaches macro F1 of 71.7, 69.9 and 70.6 (zero-shot, few-shot, all topics). These scores are 3.2, 1.5 and 2.2 points above BERT-joint-ft.
  • Matching sentiment and stance is easier to classify: for BS-RGCN, accuracy is 78.9% on (Neg, Con) versus 71.4% on (Pos, Con). It is 56.6% on (Pos, Pro) versus 47.5% on (Neg, Pro). Pro stances remain the hardest class on VAST.
  • More commonsense helps: pre-training the autoencoder on 50%, 70% and then 100% of the extracted concepts raises zero-shot macro F1 steadily for all tested variants.
  • Linguistic phenomena: the authors report gains on difficult cases such as sarcasm and quotations. Case studies show sentiment cues and implicit topic mentions (e.g., declawing linked to nail removal) leading to correct predictions.

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

18 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 15 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. arXiv 2208.08797

@inproceedings{luo2022exploiting,
  title = {{Exploiting Sentiment and Common Sense for Zero-shot Stance Detection}},
  author = {Luo, Yun and Liu, Zihan and Shi, Yuefeng and Li, Stan Z. and Zhang, Yue},
  booktitle = {Proceedings of the 29th International Conference on Computational Linguistics},
  year = {2022},
  eprint = {2208.08797},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2208.08797}
}

References

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