EXTRACTING EMOTION-CAUSE PAIRS: A BILSTM-DRIVEN METHODOLOGY

[1] Alswaidan N., Menai M. E. B.: A survey of state-of-the-art approaches for emotion recognition in text. Knowledge and Information Systems 62(8), 2020, 2937–2987 [https://doi.org/10.1007/s10115-020-01449-0].
DOI: https://doi.org/10.1007/s10115-020-01449-0   Google Scholar

[2] Bahdanau D., Cho K., Bengio Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. 2014.
  Google Scholar

[3] Chen F., Shi Z., Yang Z., Huang Y.: Recurrent synchronization network for emotion-cause pair extraction. Knowledge-Based Systems 238, 2022, 107965.
DOI: https://doi.org/10.1016/j.knosys.2021.107965   Google Scholar

[4] Chen X., Li Q., Wang J.: A unified sequence labeling model for emotion cause pair extraction. 28th International Conference on Computational Linguistics, 2020, 208–218.
DOI: https://doi.org/10.18653/v1/2020.coling-main.18   Google Scholar

[5] Cheng Z., Jiang Z., Yin Y., Yu H., Gu Q.: A symmetric local search network for emotion-cause pair extraction. 28th International Conference on Computational Linguistics, 2020, 139–149.
DOI: https://doi.org/10.18653/v1/2020.coling-main.12   Google Scholar

[6] Colombo P., Witon W., Modi A., Kennedy J., Kapadia M.: Affect-driven dialog generation. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minnesota, Minneapolis 2019, 3734–3743 [https://doi.org/10.18653/v1/N19-1374].
DOI: https://doi.org/10.18653/v1/N19-1374   Google Scholar

[7] Devlin J.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
  Google Scholar

[8] Ding Z., Xia R., Yu J.: ECPE-2D: Emotion-cause pair extraction based on joint two-dimensional representation, interaction and prediction. 58th Annual Meeting of the Association for Computational Linguistics, 2020, 3161–3170 [https://doi.org/10.18653/v1/2020.acl-main.288].
DOI: https://doi.org/10.18653/v1/2020.acl-main.288   Google Scholar

[9] Ding Z., Xia R., Yu J.: End-to-end emotion-cause pair extraction based on sliding window multi-label learning. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020, 3574–3583.
DOI: https://doi.org/10.18653/v1/2020.emnlp-main.290   Google Scholar

[10] Fan C., Yuan C., Du J., Gui L., Yang M., Xu R.: Transition-based directed graph construction for emotion-cause pair extraction. 58th Annual Meeting of the Association for Computational Linguistics, 2020, 3707–3717.
DOI: https://doi.org/10.18653/v1/2020.acl-main.342   Google Scholar

[11] Gao K., Xu H., Wang J.: A rule-based approach to emotion cause detection for chinese micro-blogs. Expert Syst. Appl. 42(9), 2015, 4517–4528.
DOI: https://doi.org/10.1016/j.eswa.2015.01.064   Google Scholar

[12] Gao Q., Hu J., Xu R., Gui L., He Y., Wong K.-F., Lu Q.: Overview of NTCIR-13 ECA Task. 13th NTCIR Conference on Evaluation of Information Access Technologies. Japan, Tokyo 2017, 361–366.
  Google Scholar

[13] Ghosh S., Chollet M., Laksana E., Morency L. P., Scherer S.: Affect-LM: A neural language model for customizable affective text generation. 55th Annual Meeting of the Association for Computational Linguistics. Canada, Vancouver 2017, 634–642 [https://doi.org/10.18653/v1/P17-1059].
DOI: https://doi.org/10.18653/v1/P17-1059   Google Scholar

[14] Gui Lin, Xu R., Wu D., Lu Q., Zhou Y.: Event-driven emotion cause extraction with corpus construction. Wong K.-F. et al. (eds): Social Media Content Analysis: Natural Language Processing and Beyond. World Scientific, 2018, 145–160.
DOI: https://doi.org/10.1142/9789813223615_0011   Google Scholar

[15] Kingma D. P., Ba J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  Google Scholar

[16] Li W. et al.: ECPEC: Emotion-Cause Pair Extraction in Conversations. IEEE Transactions on Affective Computing 14(03), 2023, 1754–1765.
DOI: https://doi.org/10.1109/TAFFC.2022.3216551   Google Scholar

[17] Li X., Song K., Feng S., Wang D., Zhang Y.: A co-attention neural network model for emotion cause analysis with emotional context awareness. Conference on Empirical Methods in Natural Language Processing, 2018, 4752–4757.
DOI: https://doi.org/10.18653/v1/D18-1506   Google Scholar

[18] Modi A., Kapadia M., Fidaleo D. A., Kennedy J. R., Witon W., Colombo P.: Affect-driven dialog generation. U.S. Patent 10,818,312 B2, October 27, 2020.
  Google Scholar

[19] Neumann M. P. M., Iyyer M., Gardner M., Clark C., Lee K., Zettlemoyer L.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365, 2018.
  Google Scholar

[20] Pennington J., Socher R., Manning C. D.: Glove: Global vectors for word representation. Conference on Empirical Methods in Natural Language processing – EMNLP, 2014, 1532–1543.
DOI: https://doi.org/10.3115/v1/D14-1162   Google Scholar

[21] Singh I., Barkati A., Goswamy T., Modi A.: Adapting a language model for controlled affective text generation. arXiv preprint arXiv:2011.04000 (2020).
  Google Scholar

[22] Strapparava C., Mihalcea R.: Learning to identify emotions in text. ACM symposium on Applied computing, 2008, 1556–1560.
DOI: https://doi.org/10.1145/1363686.1364052   Google Scholar

[23] Vaswani A. et al.: Attention is all you need. Advances in neural information processing systems. 30th Advances in Neural Information Processing Systems – NIPS, 2017, 5998–6008.
  Google Scholar

[24] Witon W., Colombo P., Modi A., Kapadia M.: Disney at IEST 2018: Predicting emotions using an ensemble. 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Belgium, Brussels 2018, 248–253 [https://doi.org/10.18653/v1/P17].
DOI: https://doi.org/10.18653/v1/W18-6236   Google Scholar

[25] Xia R., Ding Z.: Emotion-cause pair extraction: A new task to emotion analysis in texts. arXiv preprint arXiv:1906.01267 (2019).
DOI: https://doi.org/10.18653/v1/P19-1096   Google Scholar

[26] Zhang Z.: Improved adam optimizer for deep neural networks. IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), 2018.
DOI: https://doi.org/10.1109/IWQoS.2018.8624183   Google Scholar