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    <abstract>Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text. Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation. Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction. The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently. Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71% (p\textless0.01) in F1 measure.</abstract>
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%0 Conference Proceedings
%T Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction
%A Fan, Chuang
%A Yuan, Chaofa
%A Du, Jiachen
%A Gui, Lin
%A Yang, Min
%A Xu, Ruifeng
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F fan-etal-2020-transition
%X Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text. Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation. Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction. The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently. Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71% (p\textless0.01) in F1 measure.
%R 10.18653/v1/2020.acl-main.342
%U https://aclanthology.org/2020.acl-main.342
%U https://doi.org/10.18653/v1/2020.acl-main.342
%P 3707-3717

Markdown (Informal)

[Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction](https://aclanthology.org/2020.acl-main.342) (Fan et al., ACL 2020)

• Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction (Fan et al., ACL 2020)

ACL

• Chuang Fan, Chaofa Yuan, Jiachen Du, Lin Gui, Min Yang, and Ruifeng Xu. 2020. Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3707–3717, Online. Association for Computational Linguistics.