Abstract
With the development of deep learning, the method of large-scale dialogue generation based on deep learning has received extensive attention. The current research has aimed to solve the problem of the quality of generated dialogue content, but has failed to fully consider the emotional factors of generated dialogue content. In order to solve the problem of emotional response in the open domain dialogue system, we proposed a dynamic emotional session generation model (DESG). On the basis of the Seq2Seq (sequence-to-sequence) framework, the model abbreviation incorporates a dictionary-based attention mechanism that encourages the substitution of words in response with synonyms in emotion dictionaries. Meanwhile, in order to improve the model, internal emotion regulator and emotion classifier mechanisms are introduced in order to build a large-scale emotion-session generation model. Experimental results show that our DESG model can not only produce an appropriate output sequence in terms of content (related grammar) for a given post and emotion category, but can also express the expected emotional response explicitly or implicitly.
Highlights
Designing human–machine dialogue systems (HDSs) and enabling computers to interact with humans through human languages is an important and challenging task in the field of artificial intelligence
With the rapid growth of social data on the internet, data-driven open-domain dialogue systems have gradually become the focus of attention in the academic community, and HDSs have gradually changed from a service role to one of emotional partner [1]
We propose a dynamic emotional session generation model (DESG) based on the Seq2Seq model and a dictionary-based attention mechanism in response to the problem of emotional response in open-domain dialogue systems
Summary
Designing human–machine dialogue systems (HDSs) and enabling computers to interact with humans through human languages is an important and challenging task in the field of artificial intelligence. We propose a dynamic emotional session generation model (DESG) based on the Seq2Seq (sequence-to-sequence) model and a dictionary-based attention mechanism in response to the problem of emotional response in open-domain dialogue systems. The main contributions of this study can be summarized as follow: It proposes to address the emotion factor in large-scale conversation generation. It proposes an end-to-end framework (called a DESG) to incorporate emotional influence into large-scale conversation generation via two novel mechanisms: an internal emotion regulator, and an emotion classifier Experimental results with both automatic and human evaluations show that for a given post and an emotion category, our DESG can express the desired emotion explicitly (if possible) or implicitly (if necessary), while successfully generating meaningful responses with a coherent structure.
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