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    题名: IMTKU Emotional Dialogue System for Short Text Conversation at NTCIR-14 STC-3 (CECG) Task
    作者: Day, Min-Yuh;Hung, Chi-Sheng;Xie, Yi-Jun;Chen, Jhih-Yi;Kuo, Yu-Ling;Lin, Jian-Ting
    关键词: artificial intelligence;deep learning;dialogue systems;encoder-decoder;sequence-to-sequence;recurrent neural network;long short-term memory
    日期: 2019-06-10
    上传时间: 2019-10-15 12:12:29 (UTC+8)
    摘要: This paper describes the IMTKU (Information Management at Tamkang University) emotional dialogue system for Short Text Conversation at NTCIR-14 STC-3 Chinese Emotional Conversation Generation (CECG) Subtask. The IMTKU team proposed an emotional dialogue system that integrates retrieval-based model, generative-based model, and emotion classification model with deep learning approach for short text conversation focusing on Chinese emotional conversation generation subtask at NTCIR-14 STC-3 task. For the retrieval-based method, the Apache Solr search engine was used to retrieve the responses to a given post and obtain the most similar one by each emotion with a word2vec similarity ranking model. For the generative-based method, we adopted a sequence-to-sequence model for generating responses with emotion classifier to label the emotion of each response to a given post and obtain the most similar one by each emotion with a word2vec similarity ranking model. The official results show that the aver-age score of IMTKU is 0.592 for the retrieval-based model and 0.06 for the generative-based model. The IMTKU self-evaluation indicates that the average score is 1.183 for retrieval-based model and 0.1the 6 for the generative-based model. The best accuracy score of the emotion classification model of IMTKU is 87.6% with bi-directional long short-term memory (Bi-LSTM).
    關聯: Proceedings of The 14th NTCIR Conference on Evaluation of Information Access Technologies (NTCIR-14)
    显示于类别:[資訊管理學系暨研究所] 會議論文

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