TY - GEN
T1 - Generating Contextually Coherent Responses by Learning Structured Vectorized Semantics
AU - Wang, Yan
AU - Zheng, Yanan
AU - Jiang, Shimin
AU - Dong, Yucheng
AU - Chen, Jessica
AU - Wang, Shaohua
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Generating contextually coherent responses has been one of the most critical challenges in building intelligent dialogue systems. Key issues are how to appropriately encode contexts and how to make good use of them during the generation. Past works either directly use (hierarchical) RNN to encode contexts or use attention-based variants to further weight different words and utterances. They tend to learn dispersed focuses over all contextual information, which contradicts the facts that humans tend to respond to certain concentrated semantics of contexts. This leads to the results that generated responses are only show semantically related to, but not precisely coherent with the given contexts. To this end, this paper proposes a contextually coherent dialogue generation (ConDial) method by first encoding contexts into structured semantic vectors using self-attention, and then adaptively choosing key semantic vectors to guide the response generation. Based on the structured semantics, it also develops a calibration mechanism with a dynamic vocabulary during decoding, which enhances exact coherent expressions by adjusting word distribution. According to the experiments, ConDial shows better generative performance than state-of-the-arts and is capable of generating responses that not only continue the topics but also keep coherent contextual expressions.
AB - Generating contextually coherent responses has been one of the most critical challenges in building intelligent dialogue systems. Key issues are how to appropriately encode contexts and how to make good use of them during the generation. Past works either directly use (hierarchical) RNN to encode contexts or use attention-based variants to further weight different words and utterances. They tend to learn dispersed focuses over all contextual information, which contradicts the facts that humans tend to respond to certain concentrated semantics of contexts. This leads to the results that generated responses are only show semantically related to, but not precisely coherent with the given contexts. To this end, this paper proposes a contextually coherent dialogue generation (ConDial) method by first encoding contexts into structured semantic vectors using self-attention, and then adaptively choosing key semantic vectors to guide the response generation. Based on the structured semantics, it also develops a calibration mechanism with a dynamic vocabulary during decoding, which enhances exact coherent expressions by adjusting word distribution. According to the experiments, ConDial shows better generative performance than state-of-the-arts and is capable of generating responses that not only continue the topics but also keep coherent contextual expressions.
KW - Calibration mechanism
KW - Contextual coherence
KW - Dialogue generation
KW - Structured vectorized semantics
UR - http://www.scopus.com/inward/record.url?scp=85104737840&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85104737840&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-73197-7_5
DO - 10.1007/978-3-030-73197-7_5
M3 - Conference contribution
AN - SCOPUS:85104737840
SN - 9783030731960
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 70
EP - 87
BT - Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings
A2 - Jensen, Christian S.
A2 - Lim, Ee-Peng
A2 - Yang, De-Nian
A2 - Chang, Chia-Hui
A2 - Xu, Jianliang
A2 - Peng, Wen-Chih
A2 - Huang, Jen-Wei
A2 - Shen, Chih-Ya
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021
Y2 - 11 April 2021 through 14 April 2021
ER -