GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation (2203.02177v2)

Abstract: Conversations have become a critical data format on social media platforms. Understanding conversation from emotion, content and other aspects also attracts increasing attention from researchers due to its widespread application in human-computer interaction. In real-world environments, we often encounter the problem of incomplete modalities, which has become a core issue of conversation understanding. To address this problem, researchers propose various methods. However, existing approaches are mainly designed for individual utterances rather than conversational data, which cannot fully exploit temporal and speaker information in conversations. To this end, we propose a novel framework for incomplete multimodal learning in conversations, called "Graph Complete Network (GCNet)", filling the gap of existing works. Our GCNet contains two well-designed graph neural network-based modules, "Speaker GNN" and "Temporal GNN", to capture temporal and speaker dependencies. To make full use of complete and incomplete data, we jointly optimize classification and reconstruction tasks in an end-to-end manner. To verify the effectiveness of our method, we conduct experiments on three benchmark conversational datasets. Experimental results demonstrate that our GCNet is superior to existing state-of-the-art approaches in incomplete multimodal learning. Code is available at https://github.com/zeroQiaoba/GCNet.

Overview of GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation

The paper introduces GCNet, a Graph Completion Network framework devised to address the prevalent issue of incomplete modalities in multimodal conversational data. Multimodal learning in conversations is central to improving human-computer interaction, yet, real-world applications often suffer from incomplete datasets across modalities such as audio, visual, and textual due to environmental noise, sensor failures, or other disruptions. The proposed method distinguishes itself from previous approaches by specifically targeting conversational data, crucially incorporating both temporal and speaker information to enhance learning from incomplete data.

Methodology

GCNet leverages Graph Neural Networks (GNNs) to model complex interdependencies within conversational data. The framework introduces two specialized modules: the Speaker GNN (SGNN) and the Temporal GNN (TGNN). SGNN is designed to capture dependencies related to speakers—recognizing consistent expression styles of individuals, while TGNN focuses on temporal or sequential dependencies inherent in conversations—identifying semantic links across contiguous utterances.

This dual-module strategy is combined with an end-to-end joint optimization approach that concurrently addresses classification and reconstruction tasks. The result is a system that not only predicts missing data but seamlessly improves the understanding and classification of emotional content in conversations by reconstructing modalities commonly missing in practical data environments.

Experimental Validation

The paper validates GCNet by evaluating it on three benchmark datasets: IEMOCAP, CMU-MOSI, and CMU-MOSEI. GCNet consistently outperforms state-of-the-art methods, demonstrating its robustness and effectiveness across various missing data scenarios. For instance, on the IEMOCAP four-class dataset, GCNet improves upon existing systems by margins of up to 15.94% under higher missing rates. Additionally, GCNet maintains competitive performance on complete data, emphasizing its adaptability and efficacy in both incomplete and conventional datasets.

Implications and Future Directions

The successful deployment of GCNet has both theoretical and practical implications. The model extends the boundaries of graph-based learning frameworks in dealing with incomplete data, particularly highlighting the significance of considering conversational dependencies, which have often been neglected. Practically, this work has the potential to significantly enhance the robustness and accuracy of conversation-based AI systems, such as virtual assistants and customer support bots, which must frequently operate in imperfect data conditions.

Future developments might consider the integration of more sophisticated context-aware models or expanding the architecture to accommodate an even broader range of modalities. Also, additional real-world testing and adaptation may be necessary to translate these promising results from controlled datasets to diverse real-world applications. The insights and methodological advancements presented in this work lay a solid groundwork for continuous improvement and innovation in multimodal learning algorithms.