Conversational Decision-Making Model for Predicting the King’s Decision in the Annals of the Joseon Dynasty

JinYeong Bak, Alice Oh


Abstract
Styles of leaders when they make decisions in groups vary, and the different styles affect the performance of the group. To understand the key words and speakers associated with decisions, we initially formalize the problem as one of predicting leaders’ decisions from discussion with group members. As a dataset, we introduce conversational meeting records from a historical corpus, and develop a hierarchical RNN structure with attention and pre-trained speaker embedding in the form of a, Conversational Decision Making Model (CDMM). The CDMM outperforms other baselines to predict leaders’ final decisions from the data. We explain why CDMM works better than other methods by showing the key words and speakers discovered from the attentions as evidence.
Anthology ID:
D18-1115
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
956–961
Language:
URL:
https://aclanthology.org/D18-1115
DOI:
10.18653/v1/D18-1115
Bibkey:
Cite (ACL):
JinYeong Bak and Alice Oh. 2018. Conversational Decision-Making Model for Predicting the King’s Decision in the Annals of the Joseon Dynasty. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 956–961, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Conversational Decision-Making Model for Predicting the King’s Decision in the Annals of the Joseon Dynasty (Bak & Oh, EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1115.pdf
Video:
 https://aclanthology.org/D18-1115.mp4