教師資料查詢 | 類別: 會議論文 | 教師: 戴敏育 Min-Yuh Day (瀏覽個人網頁)

標題: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
語言英文
收錄於
會議性質國際
校內研討會地點
研討會時間20190730~20190801
通訊作者Chi-Sheng Hung
國別美國
公開徵稿
出版型式
出處IEEE
相關連結
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