Ding (2019)proposed the ECPE task and released a corresponding ECPE dataset based on the ECE corpus.To tackle the task,they proposed a two-step method,which first extracted emotions and causes individually by multi-task framework,and then got the emotion-cause pairs by pairing and filtering. 5 Conclusion and Future Work In this paper,we propose a symmetric local search network(SLSN)to perform end-to-end emotion-cause pair extraction.SLSN can straightly extract the emotion-cause pair through a process of local search. This is realized by designing a special component,i.e.,local pair searcher,which allows simultaneously detecting and matching the emotions and causes.Experimental results on the ECPE corpus demonstrate the effectiveness of our model. In the future,we will consider to further improve the performance of emotion extraction and cause ex- traction by employing more powerful pre-trained encoder(e.g.,BERT(Devlin et al.,2019))or designing some auxiliary tasks to utilize extra knowledge.Besides,we will further explore the process of local pair search,and seek for more advanced implementations of the local pair searcher. Acknowledgements This work is supported by National Natural Science Foundation of China under Grant Nos. 61906085,61802169,61972192,41972111;JiangSu Natural Science Foundation under Grant No. BK20180325:the Second Tibetan Plateau Scientific Expedition and Research Program under Grant No. 2019QZKK0204.This work is partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization. References Ying Chen,Sophia Yat Mei Lee,Shoushan Li,and Chu-Ren Huang.2010.Emotion cause detection with linguistic constructions.In COLING 2010,23rd International Conference on Computational Linguistics,pages 179-187. Ying Chen,Wenjun Hou,Xiyao Cheng,and Shoushan Li.2018.Joint learning for emotion classification and emotion cause detection.In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing,pages 646-651. Jacob Devlin,Ming-Wei Chang,Kenton Lee,and Kristina Toutanova.2019.BERT:pre-training of deep bidirec- tional transformers for language understanding.In Jill Burstein,Christy Doran,and Thamar Solorio,editors, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Lin- guistics:Human Language Technologies,NAACL-HLT 2019,pages 4171-4186.Association for Computational Linguistics. Zixiang Ding,Huihui He,Mengran Zhang,and Rui Xia.2019.From independent prediction to reordered predic- tion:Integrating relative position and global label information to emotion cause identification.In The Thirry- Third AAAI Conference on Artificial Intelligence,AAAI 2019,pages 6343-6350. Chuang Fan,Hongyu Yan,Jiachen Du,Lin Gui,Lidong Bing,Min Yang,Ruifeng Xu,and Ruibin Mao.2019.A knowledge regularized hierarchical approach for emotion cause analysis.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 5613-5623. Kai Gao,Hua Xu,and Jiushuo Wang.2015.Emotion cause detection for chinese micro-blogs based on ecocc model.In Advances in Knowledge Discovery and Data Mining-19th Pacific-Asia Conference,PAKDD,pages 3-14. Diman Ghazi,Diana Inkpen,and Stan Szpakowicz.2015.Detecting emotion stimuli in emotion-bearing sen- tences.In Computational Linguistics and Intelligent Text Processing,pages 152-165. Lin Gui,Li Yuan,Ruifeng Xu,Bin Liu,Qin Lu,and Yu Zhou.2014.Emotion cause detection with linguistic construction in chinese weibo text.In Natural Language Processing and Chinese Computing,pages 457-464. Lin Gui,Dongyin Wu,Ruifeng Xu,Qin Lu,and Yu Zhou.2016.Event-driven emotion cause extraction with corpus construction.In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Pro- cessing,EMNLP,pages 1639-1649. 148148 Ding (2019) proposed the ECPE task and released a corresponding ECPE dataset based on the ECE corpus. To tackle the task, they proposed a two-step method, which first extracted emotions and causes individually by multi-task framework, and then got the emotion-cause pairs by pairing and filtering. 5 Conclusion and Future Work In this paper, we propose a symmetric local search network (SLSN) to perform end-to-end emotion-cause pair extraction. SLSN can straightly extract the emotion-cause pair through a process of local search. This is realized by designing a special component, i.e., local pair searcher, which allows simultaneously detecting and matching the emotions and causes. Experimental results on the ECPE corpus demonstrate the effectiveness of our model. In the future, we will consider to further improve the performance of emotion extraction and cause ex￾traction by employing more powerful pre-trained encoder (e.g., BERT (Devlin et al., 2019)) or designing some auxiliary tasks to utilize extra knowledge. Besides, we will further explore the process of local pair search, and seek for more advanced implementations of the local pair searcher. Acknowledgements This work is supported by National Natural Science Foundation of China under Grant Nos. 61906085, 61802169, 61972192, 41972111; JiangSu Natural Science Foundation under Grant No. BK20180325; the Second Tibetan Plateau Scientific Expedition and Research Program under Grant No. 2019QZKK0204. This work is partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization. References Ying Chen, Sophia Yat Mei Lee, Shoushan Li, and Chu-Ren Huang. 2010. Emotion cause detection with linguistic constructions. In COLING 2010, 23rd International Conference on Computational Linguistics, pages 179–187. Ying Chen, Wenjun Hou, Xiyao Cheng, and Shoushan Li. 2018. Joint learning for emotion classification and emotion cause detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 646–651. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: pre-training of deep bidirec￾tional transformers for language understanding. In Jill Burstein, Christy Doran, and Thamar Solorio, editors, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Lin￾guistics: Human Language Technologies, NAACL-HLT 2019, pages 4171–4186. Association for Computational Linguistics. Zixiang Ding, Huihui He, Mengran Zhang, and Rui Xia. 2019. From independent prediction to reordered predic￾tion: Integrating relative position and global label information to emotion cause identification. In The Thirty￾Third AAAI Conference on Artificial Intelligence, AAAI 2019, pages 6343–6350. Chuang Fan, Hongyu Yan, Jiachen Du, Lin Gui, Lidong Bing, Min Yang, Ruifeng Xu, and Ruibin Mao. 2019. A knowledge regularized hierarchical approach for emotion cause analysis. 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 5613–5623. Kai Gao, Hua Xu, and Jiushuo Wang. 2015. Emotion cause detection for chinese micro-blogs based on ecocc model. In Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD, pages 3–14. Diman Ghazi, Diana Inkpen, and Stan Szpakowicz. 2015. Detecting emotion stimuli in emotion-bearing sen￾tences. In Computational Linguistics and Intelligent Text Processing, pages 152–165. Lin Gui, Li Yuan, Ruifeng Xu, Bin Liu, Qin Lu, and Yu Zhou. 2014. Emotion cause detection with linguistic construction in chinese weibo text. In Natural Language Processing and Chinese Computing, pages 457–464. Lin Gui, Dongyin Wu, Ruifeng Xu, Qin Lu, and Yu Zhou. 2016. Event-driven emotion cause extraction with corpus construction. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Pro￾cessing, EMNLP, pages 1639–1649