Sarcasm Detection Base on Adaptive Incongruity Extraction Network and Incongruity Cross-Attention
Source abstract
Abstract
Sarcasm is a linguistic phenomenon indicating a difference between literal meanings and implied intentions. It is commonly used on blogs, e-commerce platforms, and social media. Numerous NLP tasks, such as opinion mining and sentiment analysis systems, are hampered by its linguistic nature in detection. Traditional techniques concentrated mostly on textual incongruity. Recent research demonstrated that the addition of commonsense knowledge into sarcasm detection is an effective new method. However, existing techniques cannot effectively capture sentence “incongruity” information or take good advantage of external knowledge, resulting in imperfect detection performance. In this work, new modules are proposed for maximizing the utilization of the text, the commonsense knowledge, and their interplay. At first, we propose an adaptive incongruity extraction module to compute the distance between each word in the text and commonsense knowledge. Two adaptive incongruity extraction modules are applied to text and commonsense knowledge, respectively, which can obtain two adaptive incongruity attention matrixes. Therefore, each of the words in the sequence receives a new representation with enhanced incongruity semantics. Secondly, we propose the incongruity cross-attention module to extract the incongruity between the text and the corresponding commonsense knowledge, thereby allowing us to pick useful commonsense knowledge in sarcasm detection. In addition, we propose an improved gate module as a feature fusion module of text and commonsense knowledge, which determines how much information should be considered. Experimental results on publicly available datasets demonstrate the superiority of our method in achieving state-of-the-art performance on three datasets as well as enjoying improved interpretability.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sentiment Analysis and Opinion Mining · Topic Modeling · Advanced Text Analysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, China
Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Yibin 644000, China
Wuhan Jiangyang Environment Technology Co., Ltd., Wuhan 430070, China
College of Physics and Electronic Engineering, Sichuan Normal University, Chengdu 610066, China
School of Foreign Languages, Guizhou University of Finance and Economics, Guiyang 550025, China
Funding
Demonstration Project for Transfer and Transformation of Scientific and Technological Achievements in Sichuan Province · Enterprise Informatization and Measurement and Control Technology of Internet of Things Key Laboratory of Sichuan Province Open Fund · Open Fund Project of Hainan Key Laboratory of Internet Information Retrieval
References (54)
Show source-provided references
1. Ghosh, A., and Veale, T. (2017, January 7–11). Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.10.18653/v1/D17-1050 ↗
2. Tay, Y., Luu, A.T., Hui, S.C., and Su, J. (2018, January 15–20). Reasoning with Sarcasm by Reading In-Between. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia.10.18653/v1/P18-1093 ↗
3. Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N., and Huang, R. (2013, January 18–21). Sarcasm as Contrast between a Positive Sentiment and Negative Situation. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, WA, USA.
4. Joshi, A., Sharma, V., and Bhattacharyya, P. (2015, January 26–31). Harnessing Context Incongruity for Sarcasm Detection. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Beijing, China.10.3115/v1/P15-2124 ↗
5. Xiong, T., Zhang, P., Zhu, H., and Yang, Y. (2019, January 13–17). Sarcasm Detection with Self-Matching Networks and Low-Rank Bilinear Pooling. Proceedings of the The World Wide Web Conference on—WWW ’19, San Francisco, CA, USA.10.1145/3308558.3313735 ↗
6. Mishra, A., Dey, K., and Bhattacharyya, P. (August, January 30). Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification Using Convolutional Neural Network. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, BC, Canada.
7. Castro, S., Hazarika, D., Pérez-Rosas, V., Zimmermann, R., Mihalcea, R., and Poria, S. (August, January 28). Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper). Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy.
8. Cai, Y., Cai, H., and Wan, X. (August, January 28). Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy.
9. Pan, H., Lin, Z., Fu, P., Qi, Y., and Wang, W. (2020, January 16–20). Modeling Intra and Inter-Modality Incongruity for Multi-Modal Sarcasm Detection. Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, Online.10.18653/v1/2020.findings-emnlp.124 ↗
10. Liang, B., Lou, C., Li, X., Yang, M., Gui, L., He, Y., Pei, W., and Xu, R. (2022, January 22–27). Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland.10.18653/v1/2022.acl-long.124 ↗
11. Wang, J., Sun, L., Liu, Y., Shao, M., and Zheng, Z. (2022, January 22–27). Multimodal Sarcasm Target Identification in Tweets. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland.10.18653/v1/2022.acl-long.562 ↗
12. Liu, H., Wang, W., and Li, H. (2022). Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement. arXiv.10.18653/v1/2022.emnlp-main.333 ↗
13. Sarcasm Detection with Commonsense Knowledge10.1109/TASLP.2021.3120601 ↗
14. Dimensions of Commonsense Knowledge10.1016/j.knosys.2021.107347 ↗
15. Storks, S., Gao, Q., and Chai, J. (2019). Commonsense Reasoning for Natural Language Understanding: A Survey of Benchmarks, Resources, and Approaches. arXiv.
16. Veale, T., and Hao, Y. (2010). Proceedings of the 2010 Conference on ECAI 2010: 19th European Conference on Artificial Intelligence, Lisbon, Portugal, 16–20 August 2010, IOS Press.
17. Li, J., Lin, Z., Fu, P., and Wang, W. (2021, January 7–9). Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge. Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, Punta Cana, Dominican Republic.10.18653/v1/2021.findings-emnlp.104 ↗
18. Raedt, L.D. (2022). Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, Vienna, Austria, 23–29 July 2022, Morgan Kaufmann.
19. Joshi, A., Tripathi, V., Patel, K., Bhattacharyya, P., and Carman, M. (2016, January 1–5). Are Word Embedding-Based Features Useful for Sarcasm Detection?. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA.10.18653/v1/D16-1104 ↗
20. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding
21. A Multidimensional Approach for Detecting Irony in Twitter10.1007/s10579-012-9196-x ↗
22. Barbieri, F., Ronzano, F., and Saggion, H. (2014, January 9–10). Italian Irony Detection in Twitter: A First Approach. Proceedings of the First Italian Conference on Computational Linguistics CLiC-it 2014, Pisa, Italy.
23. Bouazizi, M., and Ohtsuki, T. (2015, January 25). Opinion Mining in Twitter How to Make Use of Sarcasm to Enhance Sentiment Analysis. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, New York, NY, USA.10.1145/2808797.2809350 ↗
24. González-Ibáñez, R., Muresan, S., and Wacholder, N. (2011, January 19–24). Identifying Sarcasm in Twitter: A Closer Look. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, USA.
25. Ptacek, T., Habernal, I., and Hong, J. (2014, January 23–29). Sarcasm Detection on Czech and English Twitter. Proceedings of the COLING 2014, the 25th International Conference on Computational Linguistics, Dublin, Ireland.
26. Bosselut, A., Rashkin, H., Sap, M., Malaviya, C., Celikyilmaz, A., and Choi, Y. (August, January 28). COMET: Commonsense Transformers for Automatic Knowledge Graph Construction. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy.
27. Automatic Sarcasm Detection: A Survey10.1145/3124420 ↗
28. Davidov, D., Tsur, O., and Rappoport, A. (2010, January 15–16). Semi-Supervised Recognition of Sarcastic Sentences in Twitter and Amazon. Proceedings of the Fourteenth Conference on Computational Natural Language Learning, Uppsala, Sweden.
29. Maynard, D., and Greenwood, M.A. (2014, January 26–30). Who Cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis. Proceedings of the LREC 2014: 9th Conference on Language Resources and Evaluation, Reykjavik, Iceland.
30. ICWSM—A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews
31. Rajadesingan, A., Zafarani, R., and Liu, H. (2015, January 2). Sarcasm Detection on Twitter: A Behavioral Modeling Approach. Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, Shanghai, China.10.1145/2684822.2685316 ↗
32. Barbieri, F., Saggion, H., and Ronzano, F. (2014, January 27). Modelling Sarcasm in Twitter, a Novel Approach. Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Baltimore, MD, USA.10.3115/v1/W14-2609 ↗
33. Ghosh, A., and Veale, T. (June, January 1). Fracking Sarcasm Using Neural Network. Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, San Diego, CA, USA.
34. Radford, A., and Narasimhan, K. (2022, December 14). Improving Language Understanding by Generative Pre-Training. Available online: https://www.semanticscholar.org/paper/Improving-Language-Understanding-by-Generative-Radford-Narasimhan/cd18800a0fe0b668a1cc19f2ec95b5003d0a5035.
35. Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., and Hovy, E. (2016, January 12–17). Hierarchical Attention Networks for Document Classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.10.18653/v1/N16-1174 ↗
36. Chen, H., Sun, M., Tu, C., Lin, Y., and Liu, Z. (2016, January 1–5). Neural Sentiment Classification with User and Product Attention. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA.10.18653/v1/D16-1171 ↗
37. Tay, Y., Tuan, L.A., and Hui, S.C. (2018). Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence, New Orleans, LA, USA, 2 February 2018, AAAI Press.
38. Chen, P., Sun, Z., Bing, L., and Yang, W. (2017, January 7–11). Recurrent Attention Network on Memory for Aspect Sentiment Analysis. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.10.18653/v1/D17-1047 ↗
39. CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations10.1109/TFUZZ.2021.3072492 ↗
40. An Emoji-Aware Multitask Framework for Multimodal Sarcasm Detection10.1016/j.knosys.2022.109924 ↗
41. Þór Jónsson, B., Gurrin, C., Tran, M.-T., Dang-Nguyen, D.-T., Hu, A.M.-C., Huynh Thi Thanh, B., and Huet, B. (2022). MultiMedia Modeling, Proceedings of the 28th International Conference, MMM 2022, Phu Quoc, Vietnam, 6–10 June 2022, Springer International Publishing.10.1007/978-3-030-98355-0 ↗
42. Bedi, M., Kumar, S., Akhtar, M.S., and Chakraborty, T. (2021). Multi-Modal Sarcasm Detection and Humor Classification in Code-Mixed Conversations. IEEE Trans. Affect. Comput., 1–13.10.1109/TAFFC.2021.3083522 ↗
43. Contextualized Sarcasm Detection on Twitter
44. Joshi, A., Tripathi, V., Bhattacharyya, P., and Carman, M.J. (2016, January 11–12). Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series ‘Friends’. Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, Berlin, Germany.10.18653/v1/K16-1015 ↗
45. Ghosh, D., Richard Fabbri, A., and Muresan, S. (2017, January 15–17). The Role of Conversation Context for Sarcasm Detection in Online Interactions. Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, Saarbrücken, Germany.10.18653/v1/W17-5523 ↗
46. Wang, J., Cellary, W., Wang, D., Wang, H., Chen, S.-C., Li, T., and Zhang, Y. (2015). Proceedings of the Web Information Systems Engineering—WISE 2015, Miami, FL, USA, 1–3 November 2015, Springer International Publishing.10.1007/978-3-319-26187-4 ↗
47. Liang, B., Lou, C., Li, X., Gui, L., Yang, M., and Xu, R. (2021, January 17). Multi-Modal Sarcasm Detection with Interactive In-Modal and Cross-Modal Graphs. Proceedings of the 29th ACM International Conference on Multimedia, New York, NY, USA.10.1145/3474085.3475190 ↗
48. Chakrabarty, T., Ghosh, D., Muresan, S., and Peng, N. (2020, January 5–10). R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.10.18653/v1/2020.acl-main.711 ↗
49. Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L. (2018, January 1–6). Deep Contextualized Word Representations. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), New Orleans, LA, USA.10.18653/v1/N18-1202 ↗
50. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, 4–9 December 2017, Curran Associates, Inc.
51. Ba, J.L., Kiros, J.R., and Hinton, G.E. (2022, December 12). Layer Normalization CoRR. Available online: http://arxiv.org/abs/1607.06450.
52. Khodak, M., Saunshi, N., and Vodrahalli, K. (2018, January 7–12). A Large Self-Annotated Corpus for Sarcasm. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan.
53. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8–14). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada.
54. Kingma, D.P., and Ba, J. (2015, January 7–9). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA.