Node Classification Using Gated Attention Network for Discussions

• Published in: Transactions of the Japanese Society for Artificial Intelligence Vol. 36; no. 6; pp. A-L24_1 - 8
• Main Authors: Sakai, Atsuya, Pham, Duy, Suzuki, Shota, Sato, Takumi, Kawamura, Naoki, Ito, Takayuki
• Format: Journal Article
• Language: Japanese; English
• Published: Tokyo The Japanese Society for Artificial Intelligence 01.11.2021; Japan Science and Technology Agency
• Subjects: argumentation mining; automated facilitation; Automation; Classification; Coders; machine learning; Natural language processing; Representations; Sentences; Support systems
• ISSN: 1346-0714; 1346-8030
• DOI: 10.1527/tjsai.36-6_A-L24

In recent years, large-scale discussions on theWeb have attracted much attention. In this situation, research on large-scale online consensus building support systems has been actively conducted. Facilitators are essential to discuss smoothly in the discussion. However, it is difficult for humans to play the role of facilitator in a large-scale discussion because participants post hundreds or thousands of opinions and the discussion may be held all day long. Therefore, developing automated facilitation agent that can automatically facilitate discussions is required. In order to develop automated facilitation agent, there are previous studies on extracting discussion as a graph structure. The purpose of this study is to perform node classification, which is one of the subtask of discussion structure extraction, with high accuracy. In the proposed method, we use Bidirectional Encoder Representations from Transformers (BERT), which was the state of the art in the field of natural language processing at the time of its release and is still widely studied and used today, to obtain distributed representations from sentences. We also use Gated Attention Network (GaAN), which has an attention mechanism and can propagate and calculate the information of neighboring nodes including the importance, in order to classify the obtained distributed representations. We adopt the Issue-Based Information System (IBIS) structure, which is designed to promote creative and constructive discussions, for the discussion structure. The experimental results show that our method can classify opinions with higher accuracy than previous studies and other general classification methods.