Abstract
Stance detection is an important task in opinion mining, which aims to determine whether the author of a text is in favor of, against, or neutral towards a specific target. By now, the scarcity of annotations is one of the remaining problems in stance detection. In this paper, we propose a Stance-Emotion joint Data Augmentation with Gradual Prompt-tuning (SEGP) model to address this problem. In order to generate more training samples, we propose an auxiliary sentence based Stance-Emotion joint Data Augmentation (SEDA) method, formulate data augmentation as a conditional masked language modeling task. We leverage different relations between stance and emotion to construct auxiliary sentences. SEDA generates augmented samples by predicting the masked words conditioned on both their context and auxiliary sentences. Furthermore, we propose a Gradual Prompt-tuning method to make better use of the augmented samples, which is a combination of prompt-tuning and curriculum learning. Specifically, the model starts by training on only original samples, then adds augmented samples as training progresses. Experimental results show that SEGP significantly outperforms the state-of-the-art approaches.
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References
AlDayel, A., Magdy, W.: Stance detection on social media: state of the art and trends. Inf. Process. Manage. 58(4), 102597 (2021)
Allaway, E., McKeown, K.: Zero-shot stance detection: a dataset and model using generalized topic representations. arXiv preprint arXiv:2010.03640 (2020)
Augenstein, I., Rocktäschel, T., Vlachos, A., Bontcheva, K.: Stance detection with bidirectional conditional encoding. arXiv preprint arXiv:1606.05464 (2016)
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009)
Brown, T.B., et al.: Language models are few-shot learners. arXiv preprint arXiv:2005.14165 (2020)
Chauhan, D.S., Kumar, R., Ekbal, A.: Attention based shared representation for multi-task stance detection and sentiment analysis. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. CCIS, vol. 1143, pp. 661–669. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36802-9_70
Du, J., Xu, R., He, Y., Gui, L.: Stance classification with target-specific neural attention networks. In: International Joint Conferences on Artificial Intelligence (2017)
Garg, S., Ramakrishnan, G.: Bae: Bert-based adversarial examples for text classification. arXiv preprint arXiv:2004.01970 (2020)
Glandt, K., Khanal, S., Li, Y., Caragea, D., Caragea, C.: 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, vol. 1 (2021)
Jiao, X., et al.: Tinybert: distilling bert for natural language understanding. arXiv preprint arXiv:1909.10351 (2019)
Kenton, J.D.M.W.C., Toutanova, L.K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171–4186 (2019)
Korbar, B., Tran, D., Torresani, L.: Cooperative learning of audio and video models from self-supervised synchronization. In: Advances in Neural Information Processing Systems 31 (2018)
Küçük, D., Can, F.: Stance detection: a survey. ACM Comput. Surv. (CSUR) 53(1), 1–37 (2020)
Li, Y., Caragea, C.: 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), pp. 6299–6305 (2019)
Li, Y., Caragea, C.: A multi-task learning framework for multi-target stance detection. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 2320–2326 (2021)
Li, Y., Caragea, C.: Target-aware data augmentation for stance detection. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1850–1860 (2021)
Li, Y., Cohn, T., Baldwin, T.: Robust training under linguistic adversity. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pp. 21–27 (2017)
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586 (2021)
Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Mohammad, S.M., Sobhani, P., Kiritchenko, S.: Stance and sentiment in tweets. ACM Trans. Internet Technol. (TOIT) 17(3), 1–23 (2017)
Mueller, J., Thyagarajan, A.: Siamese recurrent architectures for learning sentence similarity. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)
Müller, M., Salathé, M., Kummervold, P.E.: Covid-twitter-bert: a natural language processing model to analyse covid-19 content on twitter. arXiv preprint arXiv:2005.07503 (2020)
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Reynolds, L., McDonell, K.: Prompt programming for large language models: Beyond the few-shot paradigm. In: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–7 (2021)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)
Sobhani, P., Inkpen, D., Zhu, X.: A dataset for multi-target stance detection. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pp. 551–557 (2017)
Somasundaran, S., Wiebe, J.: Recognizing stances in ideological on-line debates. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 116–124 (2010)
Tsvetkov, Y., Faruqui, M., Ling, W., MacWhinney, B., Dyer, C.: Learning the curriculum with bayesian optimization for task-specific word representation learning. arXiv preprint arXiv:1605.03852 (2016)
Walker, M., Anand, P., Abbott, R., Grant, R.: Stance classification using dialogic properties of persuasion. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 592–596 (2012)
Wang, W.Y., Yang, D.: That’s so annoying!!!: a lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2557–2563 (2015)
Wang, X., Pham, H., Dai, Z., Neubig, G.: Switchout: an efficient data augmentation algorithm for neural machine translation. arXiv preprint arXiv:1808.07512 (2018)
Wei, J., Huang, C., Vosoughi, S., Cheng, Y., Xu, S.: Few-shot text classification with triplet networks, data augmentation, and curriculum learning. arXiv preprint arXiv:2103.07552 (2021)
Wei, J., Zou, K.: Eda: easy data augmentation techniques for boosting performance on text classification tasks. arXiv preprint arXiv:1901.11196 (2019)
Weinshall, D., Cohen, G., Amir, D.: Curriculum learning by transfer learning: Theory and experiments with deep networks. In: International Conference on Machine Learning, pp. 5238–5246. PMLR (2018)
Wu, X., Lv, S., Zang, L., Han, J., Hu, S.: Conditional bert contextual augmentation. In: International Conference on Computational Science, pp. 84–95. Springer (2019)
Zhang, B., Yang, M., Li, X., Ye, Y., Xu, X., Dai, K.: Enhancing cross-target stance detection with transferable semantic-emotion knowledge. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3188–3197 (2020)
Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. Adv. Neural. Inf. Process. Syst. 28, 649–657 (2015)
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Wang, J., Zhou, Y., Liu, Y., Zhang, W., Hu, S. (2022). SEGP: Stance-Emotion Joint Data Augmentation with Gradual Prompt-Tuning for Stance Detection. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_48
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