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Building Emotional Conversation Systems Using Multi-task Seq2Seq Learning

  • Rui Zhang
  • Zhenyu WangEmail author
  • Dongcheng Mai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10619)

Abstract

This paper describes our system designed for the NLPCC 2017 shared task on emotional conversation generation. Our model adopts a multi-task Seq2Seq learning framework to capture the textual information of post sequence and generate responses for each type of emotions simultaneously. Evaluation results suggest that our model is competitive on emotional generation, which achieves 0.9658 on average emotion accuracy. We also observe the emotional interaction in human conversation, and try to explain it as empathy at the psychological level. Finally, our model achieves 325 on total score, 0.545 on average score and won the fourth place on total score.

Keywords

Conversation generation Emotions Multi-task Seq2Seq 

Notes

Acknowledgements

This work is supported by the Science and Technology Program of Guangdong Province, China (2015B010131003). The authors also thank the editors and reviewers for their constructive editing and reviewing, respectively.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Software EngineeringSouth China University of TechnologyGuangzhouPeople’s Republic of China

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