TY - JOUR
T1 - Conditional Text Generation for Harmonious Human-Machine Interaction
AU - Guo, Bin
AU - Wang, Hao
AU - Ding, Yasan
AU - Wu, Wei
AU - Hao, Shaoyang
AU - Sun, Yueqi
AU - Yu, Zhiwen
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/3
Y1 - 2021/3
N2 - In recent years, with the development of deep learning, text-generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication. The automatically generated text is becoming more and more fluent so researchers begin to consider more anthropomorphic text-generation technology, that is, the conditional text generation, including emotional text generation, personalized text generation, and so on. Conditional Text Generation (CTG) has thus become a research hotspot. As a promising research field, we find that much attention has been paid to exploring it. Therefore, we aim to give a comprehensive review of the new research trends of CTG. We first summarize several key techniques and illustrate the technical evolution route in the field of neural text generation, based on the concept model of CTG. We further make an investigation of existing CTG fields and propose several general learning models for CTG. Finally, we discuss the open issues and promising research directions of CTG.
AB - In recent years, with the development of deep learning, text-generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication. The automatically generated text is becoming more and more fluent so researchers begin to consider more anthropomorphic text-generation technology, that is, the conditional text generation, including emotional text generation, personalized text generation, and so on. Conditional Text Generation (CTG) has thus become a research hotspot. As a promising research field, we find that much attention has been paid to exploring it. Therefore, we aim to give a comprehensive review of the new research trends of CTG. We first summarize several key techniques and illustrate the technical evolution route in the field of neural text generation, based on the concept model of CTG. We further make an investigation of existing CTG fields and propose several general learning models for CTG. Finally, we discuss the open issues and promising research directions of CTG.
KW - conditional text generation
KW - deep learning
KW - dialog systems
KW - Human-computer interaction
KW - personalization
UR - http://www.scopus.com/inward/record.url?scp=85102787123&partnerID=8YFLogxK
U2 - 10.1145/3439816
DO - 10.1145/3439816
M3 - 文章
AN - SCOPUS:85102787123
SN - 2157-6904
VL - 12
JO - ACM Transactions on Intelligent Systems and Technology
JF - ACM Transactions on Intelligent Systems and Technology
IS - 2
M1 - 14
ER -