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

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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.
Publisher
Empirical Methods in Natural Language Processing
Issue Date
2018-11
Language
English
Citation

Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.956 - 961

URI
http://hdl.handle.net/10203/288943
Appears in Collection
CS-Conference Papers(학술회의논문)
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