Application Research Of Emotional Dialogue Model Based On Deep Learning

Dialogue model can be described as a model of using natural language to generate a response to a question.Its purpose is to achieve the effect of simulating human dialogue.People have abundant emotions and dialogue is one of the most common and easiest ways to express emotions.However,under the existing deep learning framework,the study of dialogue models seldom takes the emotional factors between dialogues into account.Therefore,how to make the dialogue models' responses with emotions has gradually become one of the research hotspots of dialogue models.At present,the dialogue model usually uses Sequence to Sequence(Seq2Seq)model,which has the following disadvantages in emotional dialogue:(1)The model has no emotional encoder and decoder.The model does not consider the emotional relationship between the question and the answer.(2)The answer does not consider the context.The model only considers the relationship between the current question and the answer,and ignores the influence of the previous turns of dialogue.(3)The model is easy to generate universal answers,and there are a large number of many-to-one dialogue in the corpus that makes the answers be the same.Therefore,in order to achieve the purpose of emotional dialogue and solve the three questions,the main work of the paper are as follows:Firstly,the paper proposes a multi-turn emotional dialogue model based on Seq2 Seq,which improves the model input structure,encoder structure,decoder structure and search algorithm,making the model generate emotional,diverse and context-sensitive response.In terms of model input,the paper adds emotional information and position information into word vectors.In terms of the encoder,the paper encodes the current input and the emotion of sentence first,and generates the semantic vector.Then the model additionally encodes the context and the emotion of sentence,and generates the context vector.It adds context information while ensuring that the current input is independent.In the decoder part,doublelayered attention separately calculates the weights of the two semantic vectors in decoder.At the same time,the model increase the calculation of the emotional distance on the loss function.In search algorithms parts,the paper realizes the diversity of response when generating sentences by the expand cluster search algorithm,and maximizes emotions by weighting the emotional vocabulary.Secondly,the paper uses four objective evaluation indicators to evaluate the results of generation,the context similarity,the answer diversity,and the emotional response probability.Multi-turn emotional dialogue model increases 6% than baseline on the sentiment classifier and 7.8% over the baseline on the Natural Language Processing Toolkit sentiment classifier.The model is 1.6 higher than the baseline in the Bilingual Evaluation Understudy.It is 4% higher than the baseline in the diversity neural network classifier.Compared with the model word richness,it increases 5% than baseline.Experimental results show that the model can generate emotional,contextual,and diverse responses.Finally,based on multi-turn emotional dialogue model,the paper designs and implements multi-turn emotional dialogue system to verify the practical feasibility of the model.The system mainly includes three parts: the interactive interface,the server and the model.The way that the user chats with the machine illustrates the purpose of the multi-turn emotional dialogue model and the application scenarios,and it verifies the effectiveness of the model in practical applications.