Yoonjin Jang  Keunha Kim  Youngjoong Ko

10.5626/JOK.2024.51.10.918

multi-party dialogue discourse parsing addressee prediction reply-to prediction

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.