淡江大學機構典藏:Item 987654321/117414
English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 62822/95882 (66%)
造訪人次 : 4028187      線上人數 : 567
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧：
  • 您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料
    •  進階搜尋


    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/117414


    題名: 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
    關鍵詞: artificial intelligence;deep learning;dialogue systems;encoder-decoder;sequence-to-sequence;recurrent neural network;long short-term memory
    日期: 2019-06-10
    上傳時間: 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)
    顯示於類別:[資訊管理學系暨研究所] 會議論文

    文件中的檔案:

    檔案 大小格式瀏覽次數
    index.html0KbHTML218檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋