Leveraging hierarchical semantic‐emotional memory in emotional conversation generation

Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the gener-ation of semantically coherent and emotionally reasonable responses.However,most previous works generate emotional responses solely from input posts,which do not take full advantage of the training corpus and suffer from generating generic responses.In this study,we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation(called HSEMEC),which can learn abstract semantic conver-sation patterns and emotional information from the large training corpus.The learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation generation.Comprehensive experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual evaluation.For reproducibility,we release the code and data publicly at:https://github.com/siat‐nlp/HSEMEC‐code‐data.

Emotional dialog generation via multiple classifiers based on a generative adversarial network

Background Human-machine dialog generation is an essential topic of research in the field of natural language processing.Generating high-quality,diverse,fluent,and emotional conversation is a challenging task.Based on continuing advancements in artificial intelligence and deep learning,new methods have come to the forefront in recent times.In particular,the end-to-end neural network model provides an extensible conversation generation framework that has the potential to enable machines to understand semantics and automatically generate responses.However,neural network models come with their own set of questions and challenges.The basic conversational model framework tends to produce universal,meaningless,and relatively"safe"answers.Methods Based on generative adversarial networks(GANs),a new emotional dialog generation framework called EMC-GAN is proposed in this study to address the task of emotional dialog generation.The proposed model comprises a generative and three discriminative models.The generator is based on the basic sequence-to-sequence(Seq2Seq)dialog generation model,and the aggregate discriminative model for the overall framework consists of a basic discriminative model,an emotion discriminative model,and a fluency discriminative model.The basic discriminative model distinguishes generated fake sentences from real sentences in the training corpus.The emotion discriminative model evaluates whether the emotion conveyed via the generated dialog agrees with a pre-specified emotion,and directs the generative model to generate dialogs that correspond to the category of the pre-specified emotion.Finally,the fluency discriminative model assigns a score to the fluency of the generated dialog and guides the generator to produce more fluent sentences.Results Based on the experimental results,this study confirms the superiority of the proposed model over similar existing models with respect to emotional accuracy,fluency,and consistency.Conclusions The proposed EMC-GAN model is capable of generating consistent,smooth,and fluent dialog that conveys pre-specified emotions,and exhibits better performance with respect to emotional accuracy,consistency,and fluency compared to its competitors.

A Facial Expression Emotion Recognition Based Human-robot Interaction System

A facial expression emotion recognition based human-robot interaction(FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on2D-Gabor, uniform local binary pattern(LBP) operator, and multiclass extreme learning machine(ELM) classifier is presented,which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios,i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEERHRI system can be applied in home service, smart home, safe driving, and so on.

Robot’s Emotion Decision by Energy and Entropy Concepts

This study is to introduce concepts of energy and entropy to describe a robot＇s emoton decisien. It chooses the dimensional approach based on factors of pleasure and arousal for the merit of the interpolation between enotions. Especially, Circumplex model which has also two axes： pleasure and arousal is used. Besides, the model indicates how emotions are distributed in the two-dimensional plane. Then by the definition of psychodynamicsthe energy states （mental energy and physical energy） are matched to pleasure and arousal respectively that are the basis of Circumplex model. The mental energy is updated by the result of Prospect theory which measures the value of gain and loss as pleasure factor. And the physical energy is updated by the result of hedonic scaling which measures levels of arousal from pleasure computed by Prospect theory, and the result of intensity of stimuli. Then the energy states are fed by entropy. The feedback loop by entropy satisfies the 2nd law of thermodynamics. The energy states generated by stimuli and fed by entropy take a position in the plane of Circumplex model. Then distances between the current position and other emotions are cornputed to get a level of each emotion, proportional to the inverse of the distance.