Elamur Research⌕
☰
home / papers / 2023 / Adversarial Network with External Knowledge for Zero-Shot Stance Detection

Adversarial Network with External Knowledge for Zero-Shot Stance Detection

Summary

The paper tackles zero-shot stance detection, which means predicting whether a text is favorable, opposed, or neutral toward a target that never appears in training. The authors argue that earlier systems either relied on external knowledge or on adversarial target-invariant learning, but not both. Their model, ANEK, encodes the text and target with BERT and a sentiment-aware SentiBERT, merging the two through cross-attention. Supervised stance contrastive learning sharpens class separation. A feature separator then splits target-specific from target-invariant features, and a target discriminator trains the invariant part adversarially. Common sense knowledge comes from a ConceptNet subgraph, which is encoded by a graph autoencoder (RGCN encoder, DistMult decoder) and fused into the text representation. The model was evaluated on SEM16, WT-WT and COVID-19. It performs competitively, and ablations show that every component contributes. The work matters because it shows how sentiment cues and background world knowledge help a model infer stances that are only implied, for example when a target is never named.

Key points

  • Seed words and subgraph: Seed words are unique nouns, adjectives and adverbs drawn from all targets' data. All ConceptNet triples one hop from these seeds make up the knowledge subgraph.
  • Ablation removing the target discriminator: On SEM16, the Donald Trump target falls from 50.3 to 47.8 F1, the steepest drop for that target.
  • Ablation removing contrastive loss, sentiment, or common sense: Removing any of these also lowers scores consistently across SEM16, WT-WT and COVID-19.
  • Cross-target transfer on SEM16: ANEK reaches 56.4 F1avg for DT→HC, beating PT-HCL (55.3) and TPDG (52.9). On FM→LA it scores 58.5, slightly below PT-HCL's 59.3.
  • Case study: ANEK handles a sarcastic text and an implicit "Republican"–Trump link that BERT and TOAD misclassify. Very short texts with complex background remain hard, and the authors propose data augmentation as future work.

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

Cite

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

@inproceedings{wang2023adversarial,
  title = {{Adversarial Network with External Knowledge for Zero-Shot Stance Detection}},
  author = {Wang, Chunling and Zhang, Yijia and Yu, Xingyu and Liu, Guantong and Chen, Fei and Lin, Hongfei},
  booktitle = {Proceedings of the 22nd Chinese National Conference on Computational Linguistics},
  year = {2023},
  doi = {10.1007/978-981-99-6207-5\_26},
  url = {https://doi.org/10.1007/978-981-99-6207-5_26}
}

References

30 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Emily Allaway and Kathleen Mckeown. 2020. Zero-shot stance detection: A dataset and model using generalized topic representations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8913–8931. find our card
  2. Emily Allaway, Malavika Srikanth, and Kathleen Mckeown. 2021. Adversarial learning for zero-shot stance detection on social media. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4756–4767. find our card
  3. Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, and Kalina Bontcheva. 2016. Stance detection with bidirectional conditional encoding. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 876–885. find our card
  4. Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, and Nigel Collier. 2020. Will-they-won’t-they: A very large dataset for stance detection on twitter. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1715… find our card
  5. Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-adversarial training of neural networks. The journal of machine learning research, 17(1):2096–2030. find
  6. Proceedings of the 22nd China National Conference on Computational Linguistics, pages 824-835, Harbin, China, August 3 - 5, 2023. (c) Technical Committee on Computational Linguistics, Chinese Information Processing Society of China 833 find
  7. Deepanway Ghosal, Devamanyu Hazarika, Abhinaba Roy, Navonil Majumder, Rada Mihalcea, and Soujanya Poria. 2020. Kingdom: Knowledge-guided domain adaptation for sentiment analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3198–3210. find
  8. Kyle Glandt, Sarthak Khanal, Yingjie Li, Doina Caragea, and Cornelia Caragea. 2021. Stance detection in covid-19 tweets. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Lo… find our card
  9. Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial networks (2014). arXiv preprint arXiv:1406.2661, 1406. find
  10. Momchil Hardalov, Arnav Arora, Preslav Nakov, and Isabelle Augenstein. 2022. Few-shot cross-lingual stance detection with sentiment-based pre-training. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 10729–10737. find
  11. Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, pages 4171–4186. find
  12. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. find
  13. Dilek Küçük and Fazli Can. 2020. Stance detection: A survey. ACM Computing Surveys (CSUR), 53(1):1–37. find
  14. Sumeet Kumar and Kathleen M Carley. 2019. Tree lstms with convolution units to predict stance and rumor veracity in social media conversations. In Proceedings of the 57th annual meeting of the association for computational linguistics, pages 5047–5058. find
  15. Mirko Lai, Alessandra Teresa Cignarella, Delia Irazú Hernández Farı́as, Cristina Bosco, Viviana Patti, and Paolo Rosso. 2020. Multilingual stance detection in social media political debates. Computer Speech & Language, 63:101075. find
  16. Yingjie Li and Cornelia Caragea. 2019. Multi-task stance detection with sentiment and stance lexicons. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pages 6299–6305. find our card
  17. Bin Liang, Yonghao Fu, Lin Gui, Min Yang, Jiachen Du, Yulan He, and Ruifeng Xu. 2021. Target-adaptive graph for cross-target stance detection. In Proceedings of the Web Conference 2021, pages 3453–3464. find
  18. Bin Liang, Zixiao Chen, Lin Gui, Yulan He, Min Yang, and Ruifeng Xu. 2022. Zero-shot stance detection via contrastive learning. In Proceedings of the ACM Web Conference 2022, pages 2738–2747. find
  19. Rui Liu, Zheng Lin, Yutong Tan, and Weiping Wang. 2021. Enhancing zero-shot and few-shot stance detection with commonsense knowledge graph. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 3152–3157. find our card
  20. Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016. Semeval-2016 task 6: Detecting stance in tweets. In Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pages 31–41. find
  21. Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15, pages 593–60… find
  22. Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. 2017. Adversarial discriminative domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7167–7176. find
  23. Zhen Wang, Qiansheng Wang, Chengguo Lv, Xue Cao, and Guohong Fu. 2020. Unseen target stance detection with adversarial domain generalization. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE. find
  24. Penghui Wei and Wenji Mao. 2019. Modeling transferable topics for cross-target stance detection. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1173–1176. find
  25. Feng Xie, Zhong Zhang, Xuechen Zhao, Jiaying Zou, Bin Zhou, and Yusong Tan. 2022. Adversarial learning-based stance classifier for covid-19-related health policies. arXiv e-prints, pages arXiv–2209. find
  26. Proceedings of the 22nd China National Conference on Computational Linguistics, pages 824-835, Harbin, China, August 3 - 5, 2023. (c) Technical Committee on Computational Linguistics, Chinese Information Processing Society of China 834 find
  27. Chang Xu, Cecile Paris, Surya Nepal, and Ross Sparks. 2018. Cross-target stance classification with self-attention networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 778–783. find
  28. Jie Zhou, Junfeng Tian, Rui Wang, Yuanbin Wu, Wenming Xiao, and Liang He. 2020. Sentix: A sentiment-aware pre-trained model for cross-domain sentiment analysis. In Proceedings of the 28th international conference on computational linguistics, pages 568–579. find
  29. Qinglin Zhu, Bin Liang, Jingyi Sun, Jiachen Du, Lanjun Zhou, and Ruifeng Xu. 2022. Enhancing zero-shot stance detection via targeted background knowledge. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2070–2075. find
  30. Proceedings of the 22nd China National Conference on Computational Linguistics, pages 824-835, Harbin, China, August 3 - 5, 2023. (c) Technical Committee on Computational Linguistics, Chinese Information Processing Society of China 835 find

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/papers/wang-2023-adversarial-network-external-knowledge-zero/