Deep learning based response generation using emotion feature extraction

Abstract

In this thesis, we propose a deep learning based emotional response generation model by using the emotion feature extraction. Neural response generation is to generate human-like response given human utterance by using a deep learning. In the previous studies, expressing emotion in response generation improve user performance, user engagement, and user satisfaction. Also, the conversational agents can communicate with users at the human level. However, the previous emotional response generation model cannot understand the subtle part of emotions, because this model use the desired emotion of response as a token form. Moreover, this model is difficult to generate natural responses related to input utterance at the content level, since the information of input utterance can be biased to the emotion token. To overcome these limitations, we propose an emotional response generation model which generates emotional and natural responses by using the emotion feature extraction. Our model consists of two parts: Extraction part and Generation part. The extraction part is to extract the emotion of input utterance as a vector form by using the pre-trained LSTM based classification model. The generation part is to generate an emotional and natural response to the input utterance by reflecting the emotion vector from the extraction part and the thought vector from the encoder. We evaluate our model on the emotion-labeled dialogue dataset: DailyDialog. We evaluate our model on quantitative analysis and qualitative analysis: emotion classification

response generation modeling

user study

comparative study. In general, experiments show that the proposed model can generate emotional and natural responses.