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Emotional Human-Machine Conversation Generation Based on Long Short-Term Memory

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Abstract

With the rise in popularity of artificial intelligence, the technology of verbal communication between man and machine has received an increasing amount of attention, but generating a good conversation remains a difficult task. The key factor in human-machine conversation is whether the machine can give good responses that are appropriate not only at the content level (relevant and grammatical) but also at the emotion level (consistent emotional expression). In our paper, we propose a new model based on long short-term memory, which is used to achieve an encoder-decoder framework, and we address the emotional factor of conversation generation by changing the model’s input using a series of input transformations: a sequence without an emotional category, a sequence with an emotional category for the input sentence, and a sequence with an emotional category for the output responses. We perform a comparison between our work and related work and find that we can obtain slightly better results with respect to emotion consistency. Although in terms of content coherence our result is lower than those of related work, in the present stage of research, our method can generally generate emotional responses in order to control and improve the user’s emotion. Our experiment shows that through the introduction of emotional intelligence, our model can generate responses appropriate not only in content but also in emotion.

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  1. http://tcci.ccf.org.cn/conference/2017/cfpt.php?from=groupmessage&isappinstalled=0

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Funding

This work is supported by the Natural Science Foundation of Anhui Province (1508085QF119) and the State Key Program of the National Natural Science of China (61432004, 71571058, 61461045). This work was partially supported by the China Postdoctoral Science Foundation funded project (2015M580532, 2017T100447). This research has been partially supported by the National Natural Science Foundation of China under Grant No.61472117.

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, TunXi Road No. 193, Hefei, Anhui, China

    Xiao Sun & Shuai Ding

  2. School of Computer and Information, Hefei University of Technology, TunXi Road No. 193, Hefei, Anhui, China

    Xiao Sun & Xiaoqi Peng

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  1. Xiao Sun
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  2. Xiaoqi Peng
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  3. Shuai Ding
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Correspondence to Xiao Sun.

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The authors declare that they have no conflict of interest.

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Sun, X., Peng, X. & Ding, S. Emotional Human-Machine Conversation Generation Based on Long Short-Term Memory. Cogn Comput 10, 389–397 (2018). https://doi.org/10.1007/s12559-017-9539-4

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