GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Machine Learning

Background:

• Social media conversations are crucial data formats for human-computer interaction.
• Incomplete modalities present a significant challenge in understanding conversational data.
• Existing methods often fail to leverage temporal and speaker information inherent in conversations.

Purpose of the Study:

• To propose a novel framework, Graph Complete Network (GCNet), for incomplete multimodal learning in conversations.
• To address the limitations of existing approaches by incorporating temporal and speaker dependencies.
• To improve the accuracy and robustness of conversation understanding with missing data.

Main Methods:

• Developed GCNet, a framework utilizing two graph neural network (GNN) modules: Speaker GNN and Temporal GNN.
• Speaker GNN and Temporal GNN are designed to capture speaker and temporal dependencies within conversations.
• Employed joint optimization of classification and reconstruction tasks for end-to-end learning using both complete and incomplete data.

Main Results:

• GCNet demonstrated superior performance compared to state-of-the-art approaches on three benchmark conversational datasets.
• The proposed framework effectively handles incomplete multimodal data in conversational contexts.
• Experimental results validate the effectiveness of GCNet in capturing essential conversational dynamics.

Conclusions:

• GCNet offers a significant advancement in multimodal conversation understanding, particularly in scenarios with missing data.
• The framework's ability to exploit temporal and speaker information is key to its improved performance.
• GCNet provides a robust solution for real-world applications requiring comprehensive conversation analysis.