1School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232001, China 2School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China 3Artificial Intelligence Research Institute, Hefei Comprehensive National Science Center, Hefei 230026, China 4School of Computer Science, Huainan Normal University, Huainan 232038, China
[Objective] This paper explores new algorithms to extract emotion-cause pairs based on multi-label Seq2Seq model. [Methods] First, we used the BERT pre-training to obtain semantically rich word vectors. Then, we utilized the Bi-GRU and LSTM to obtain the global and local features of the texts. Finally, we introduced the hybrid attention mechanism to merge the features and improve the integrity of these semantic features. [Results] Compared with the latest FSS-GCN model, the F1 value of our new model for emotional cause pairs increased by 0.98 percentage point and 11.60 percentage point on two data sets. The F1 value of emotion extraction increased by 0.87 percentage point and 1.10 percentage point, while the F1 value for cause extraction increased by 0.79 percentage point and 2.31 percentage point respectively. [Limitations] Our new model mainly examined the explicit emotion-cause pairs and did not explore implicit emotion-cause pairs. [Conclusions] The proposed model improves the F1 values of extracting emotion-cause pairs.
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