Memory Fusion Network for Multi-view Sequential Learning


  • Amir Zadeh Carnegie Mellon University
  • Paul Pu Liang Carnegie Mellon University
  • Navonil Mazumder Instituto Polite ́cnico Nacional
  • Soujanya Poria Nanyang Technological University
  • Erik Cambria Nanyang Technological University
  • Louis-Philippe Morency Carnegie Mellon University


Sentiment Analysis, Emotion Recognition, Personality Traits Recognition, Multimodal Fusion


Multi-view sequential learning is a fundamental problem in machine learning dealing with multi-view sequences. In a multi-view sequence, there exists two forms of interactions between different views: view-specific interactions and cross-view interactions. In this paper, we present a new neural architecture for multi-view sequential learning called the Memory Fusion Network (MFN) that explicitly accounts for both interactions in a neural architecture and continuously models them through time. The first component of the MFN is called the System of LSTMs, where view-specific interactions are learned in isolation through assigning an LSTM function to each view. The cross-view interactions are then identified using a special attention mechanism called the Delta-memory Attention Network (DMAN) and summarized through time with a Multi-view Gated Memory. Through extensive experimentation, MFN is compared to various proposed approaches for multi-view sequential learning on multiple publicly available benchmark datasets. MFN outperforms all the multi-view approaches. Furthermore, MFN outperforms all current state-of-the-art models, setting new state-of-the-art results for all three multi-view datasets.




How to Cite

Zadeh, A., Liang, P. P., Mazumder, N., Poria, S., Cambria, E., & Morency, L.-P. (2018). Memory Fusion Network for Multi-view Sequential Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from