TY - GEN
T1 - Getting Your Conversation on Track: Estimation of Residual Life for Conversations
AU - Lu, Zexin
AU - Li, Jing
AU - Zhang, Yingyi
AU - Zhang, Haisong
N1 - Funding Information:
Jing Li and Zexin Lu are supported by PolyU Internal Fund: 1-BE2W.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/19
Y1 - 2021/1/19
N2 - This paper presents a predictive study on the progress of conversations. Specifically, we estimate the residual life for conversations, which is defined as the count of new turns to occur in a conversation thread. While most previous work focus on coarse-grained estimation that classifies the number of coming turns into two categories, we study fine-grained categorization for varying lengths of residual life. To this end, we propose a hierarchical neural model that jointly explores indicative representations from the content in turns and the structure of conversations in an end-to-end manner. Extensive experiments on both human-human and human-machine conversations demonstrate the superiority of our proposed model and its potential helpfulness in chatbot response selection.
AB - This paper presents a predictive study on the progress of conversations. Specifically, we estimate the residual life for conversations, which is defined as the count of new turns to occur in a conversation thread. While most previous work focus on coarse-grained estimation that classifies the number of coming turns into two categories, we study fine-grained categorization for varying lengths of residual life. To this end, we propose a hierarchical neural model that jointly explores indicative representations from the content in turns and the structure of conversations in an end-to-end manner. Extensive experiments on both human-human and human-machine conversations demonstrate the superiority of our proposed model and its potential helpfulness in chatbot response selection.
KW - Conversation Understanding
KW - Dialogue System
KW - Natural Language Processing
KW - Social Computing
KW - User Behavior Analysis
UR - http://www.scopus.com/inward/record.url?scp=85103925311&partnerID=8YFLogxK
U2 - 10.1109/SLT48900.2021.9383544
DO - 10.1109/SLT48900.2021.9383544
M3 - Conference article published in proceeding or book
AN - SCOPUS:85103925311
T3 - 2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings
SP - 1036
EP - 1043
BT - 2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Spoken Language Technology Workshop, SLT 2021
Y2 - 19 January 2021 through 22 January 2021
ER -