TY - GEN
T1 - Applying Emotional Keyphrase Correlation for Diversity Enhancement in Empathetic Dialogue Response Generation
AU - Chang, Jeremy
AU - Wu, Chung Hsien
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Developing an empathetic dialogue system has been facing challenges for years. Although most of the responses generated from the existing empathetic dialogue systems are to some extent grammatical and emotional, they are still far from human standards. The system-generated responses are usually uncorrelated to the user's emotional contexts. In this paper, we propose an approach to automatic extraction of the emotional keyphrases from the context, then the emotional keyphrases are fed into the Conditional Transformer language model as the conditional information. We also design a loss function called Keyphrase Correlation (KPC) to guide the model to generate responses with relevant emotional keyphrases. As a result, the diversity of the generated responses is highly enhanced. The experimental results also verified the effectiveness in terms of empathy capability and emotional correlation in the user-system conversation.
AB - Developing an empathetic dialogue system has been facing challenges for years. Although most of the responses generated from the existing empathetic dialogue systems are to some extent grammatical and emotional, they are still far from human standards. The system-generated responses are usually uncorrelated to the user's emotional contexts. In this paper, we propose an approach to automatic extraction of the emotional keyphrases from the context, then the emotional keyphrases are fed into the Conditional Transformer language model as the conditional information. We also design a loss function called Keyphrase Correlation (KPC) to guide the model to generate responses with relevant emotional keyphrases. As a result, the diversity of the generated responses is highly enhanced. The experimental results also verified the effectiveness in terms of empathy capability and emotional correlation in the user-system conversation.
UR - http://www.scopus.com/inward/record.url?scp=85143987862&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143987862&partnerID=8YFLogxK
U2 - 10.1109/IALP57159.2022.9961292
DO - 10.1109/IALP57159.2022.9961292
M3 - Conference contribution
AN - SCOPUS:85143987862
T3 - 2022 International Conference on Asian Language Processing, IALP 2022
SP - 286
EP - 291
BT - 2022 International Conference on Asian Language Processing, IALP 2022
A2 - Tong, Rong
A2 - Lu, Yanfeng
A2 - Dong, Minghui
A2 - Gong, Wengao
A2 - Li, Haizhou
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Asian Language Processing, IALP 2022
Y2 - 27 October 2022 through 28 October 2022
ER -