Dialogue-Learning Correlations in Spoken Dialogue Tutoring

被引:0
|
作者
Forbes-Riley, Kate [1 ]
Litman, Diane [1 ]
Huettner, Alison [1 ]
Ward, Arthur [1 ]
机构
[1] Univ Pittsburgh, Ctr Learning Res & Dev, Pittsburgh, PA 15260 USA
基金
美国国家科学基金会;
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We examine correlations between dialogue characteristics and learning in two corpora of spoken tutoring dialogues: a human-human corpus and a human-computer corpus, both of which have been manually annotated with dialogue acts relative to the tutoring domain. The results from our human-computer corpus show that the presence of student utterances that display reasoning, as well as the presence of reasoning questions asked by the computer tutor, both positively correlate with learning. The results from our human-human corpus show that the introduction of a new concept into the dialogue by students positively correlates with learning, but student attempts at deeper reasoning do not, and the human tutor's attempts to direct the dialogue negatively correlate with learning.
引用
收藏
页码:225 / 232
页数:8
相关论文
共 50 条
  • [31] User Simulation for Spoken Dialogue Systems: Learning and Evaluation
    Georgila, Kallirroi
    Henderson, James
    Lenzon, Oliver
    INTERSPEECH 2006 AND 9TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING, VOLS 1-5, 2006, : 1065 - 1068
  • [32] Evaluation of a hierarchical reinforcement learning spoken dialogue system
    Cuayahuitl, Heriberto
    Renals, Steve
    Lemon, Oliver
    Shimodaira, Hiroshi
    COMPUTER SPEECH AND LANGUAGE, 2010, 24 (02): : 395 - 429
  • [33] Dialogue modes in expert tutoring
    Cade, Whitney L.
    Copeland, Jessica L.
    Person, Natalie K.
    D'Mello, Sidney K.
    INTELLIGENT TUTORING SYSTEM, PROCEEDINGS, 2008, 5091 : 470 - +
  • [34] Improving the efficiency of dialogue in tutoring
    Kopp, Kristopher J.
    Britt, M. Anne
    Millis, Keith
    Graesser, Arthur C.
    LEARNING AND INSTRUCTION, 2012, 22 (05) : 320 - 330
  • [35] Learning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems
    Su, Pei-Hao
    Vandyke, David
    Gasic, Milica
    Kim, Dongho
    Mrksic, Nikola
    Wen, Tsung-Hsien
    Young, Steve
    16TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2015), VOLS 1-5, 2015, : 2007 - 2011
  • [36] Dialogue Act Annotation for Statistically Managed Spoken Dialogue Systems
    Ohtake, Kiyonori
    Misu, Teruhisa
    Hori, Chiori
    Kashioka, Hideki
    Nakamura, Satoshi
    PROCEEDINGS OF THE SECOND INTERNATIONAL SYMPOSIUM ON UNIVERSAL COMMUNICATION, 2008, : 416 - 422
  • [37] Automatically training a problematic dialogue predictor for a spoken dialogue system
    Walker, MA
    Langkilde-Geary, I
    Hastie, HW
    Gorin, A
    JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2002, 16 : 293 - 319
  • [38] Evaluating automatic dialogue strategy adaptation for a spoken dialogue system
    Chu-Carroll, J
    Nickerson, JS
    6TH APPLIED NATURAL LANGUAGE PROCESSING CONFERENCE/1ST MEETING OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE AND PROCEEDINGS OF THE ANLP-NAACL 2000 STUDENT RESEARCH WORKSHOP, 2000, : A202 - A209
  • [39] Automatically training a problematic dialogue predictor for a spoken dialogue system
    Walker, M.A. (WALKER@RESEARCH.ATT.COM), 1600, AI Access Foundation (16):
  • [40] A hybrid dialogue management approach for a flight spoken dialogue system
    Wu, XJ
    Zheng, F
    Wu, WH
    2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS, 2002, : 824 - 829