AI Affective Conversational Robot with Hybrid Generative-based and Retrieval-based Dialogue Models
學年 107
學期 2
發表日期 2019-07-30
作品名稱 AI Affective Conversational Robot with Hybrid Generative-based and Retrieval-based Dialogue Models
作品名稱（其他語言）
著者 Min-Yuh Day; Chi-Sheng Hung
作品所屬單位
出版者
會議名稱 The 20th IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI 2019)
會議地點 Los Angeles, CA, USA
摘要 ChatBot technology has become a widely used in various application fields. An important topic in the research on conversational robots is the improvement of their temperature during operation for enhanced user interaction. In this study, we propose an artificial intelligence affective conversational robot (AIACR), which is an integration of an artificial intelligence deep learning sentiment analysis model and generative-and retrieval-based dialogue models. The sentiment analysis model developed in this study uses three models, namely, multilayer perceptron (MLP), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM). Moreover, word2vec and semantics are utilized as the basis for similarity ranking models. The deep learning dialogue model, sentiment analysis model, and similarity model were integrated and compared as well. The experimental results show that the sentiment analysis model, similarity model, and dialogue model respectively utilize BiLSTM, word2vec, and the retrieval-based model to achieve the best dialogue performance. The major research contributions of this study are the developed AIACR and the proposed affective conversational robot index (ACR Index) as a criterion for evaluating the effectiveness of emotional dialogue robots.
關鍵字 Artificial Intelligence;ChatBot;Deep Learning;Natural Language Processing;Sentiment Analysis
語言 en
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20190730~20190801
通訊作者 Chi-Sheng Hung
國別 USA
公開徵稿
出版型式
出處 IEEE
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/117644 )