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).
Proceedings of The 14th NTCIR Conference on Evaluation of Information Access Technologies (NTCIR-14)