TY  - JOUR
T1  - Expected Addressee and Target Utterance Prediction for Construction of Multi-Party Dialogue Systems
AU  - Jang, Yoonjin 
AU  - Kim, Keunha 
AU  - Ko, Youngjoong 
JO  - Journal of KIISE, JOK
PY  - 2024
DA  - 2024/1/14
DO  - 10.5626/JOK.2024.51.10.918
KW  - multi-party dialogue
KW  - discourse parsing
KW  - addressee prediction
KW  - reply-to prediction
AB  - As the number of communication channels between people has increased in recent years,
there has been a rise in both multi-party conversations and one-to-one conversations. Research on
analyzing multi-party conversations has also been active. In the past, models for analyzing such
dialogues typically predicted the addressee of the final response based on the previous responses.
However, this differs from the task of generating multi-party dialogue responses, which requires the
speaker to select the addressee to whom they will respond. In this paper, we propose a new task for predicting the addressee of a multi-party dialogue that does not rely on response information. Our task
aims to predict and match the expected target utterance with the expected addressee in a real
multi-party dialogue. To accomplish this, we introduce a model that uses a transform encoder-based
masked token prediction learning method. This model predicts the expected target utterance and the
expected addressee of the current speaker based on the previous dialogue context, without considering
the final response. The proposed model achieves an accuracy of 82% in predicting the expected
recipient and 68% in predicting the expected target utterance accuracy on the Ubuntu IRC dataset.
These results demonstrate the potential of our model for use in a multi-party dialogue system, as it
can accurately predict the target utterance that should be used. Moving forward, we plan to expand
our research by creating additional datasets for multi-party dialogues and applying them to real-world
multilateral dialogue response generation systems.