Memory Fusion Network for Multi-view Sequential Learning (1802.00927v1)

Abstract: 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 existing multi-view approaches. Furthermore, MFN outperforms all current state-of-the-art models, setting new state-of-the-art results for these multi-view datasets.

• The paper introduces the Memory Fusion Network (MFN), a novel architecture that integrates view-specific LSTMs, Delta-memory Attention, and a Multi-view Gated Memory to capture temporal dynamics.
• MFN achieves significant improvements in sentiment analysis, emotion recognition, and speaker trait recognition by effectively modeling both intra-view and cross-view interactions.
• Ablation studies reveal that the Delta-memory Attention and Multi-view Gated Memory are critical for leveraging temporal information, ensuring robust performance across tasks.

Memory Fusion Network for Multi-view Sequential Learning

The paper "Memory Fusion Network for Multi-view Sequential Learning" presents a novel neural architecture designed to address the complexities inherent in multi-view sequential learning. This domain of machine learning deals with data sequences that originate from multiple perspectives, each contributing unique and, at times, non-overlapping information. Such complexities demand sophisticated mechanisms to effectively model both view-specific and cross-view interactions over time.

Overview of the Memory Fusion Network (MFN)

The core contribution of this paper is the Memory Fusion Network (MFN), a structured architecture comprising three critical components. Firstly, the System of LSTMs is deployed to independently capture view-specific dynamics. Here, each view is managed by an assigned LSTM, allowing for isolated learning of intra-view interactions.

In the second stage, cross-view interactions are detected and modeled through the Delta-memory Attention Network (DMAN). The DMAN operates on the temporally adjacent memories to effectively highlight dimensions representing cross-view interactions, ensuring that temporal dependencies are maintained and leveraged.

The final component, the Multi-view Gated Memory, serves as the unifying memory module. It integrates both the output from the DMAN and the evolving states of the System of LSTMs, thereby maintaining an up-to-date representation of cross-view dynamics throughout the sequence.

Experimental Validation and Findings

The MFN's capabilities were examined across multiple publicly available datasets spanning sentiment analysis, emotion recognition, and speaker trait recognition. Notably, in these evaluations, MFN consistently set new performance benchmarks, outperforming existing models in both classification metrics (such as binary accuracy and F1 score) and regression tasks (such as mean absolute error).

Significantly, ablation studies highlighted the critical role of each MFN component. The inclusion of the Delta-memory Attention Network, for instance, was shown to be vital in effectively leveraging temporal information for accurate cross-view interaction modeling. Similarly, the Multi-view Gated Memory demonstrated its essential role in maintaining and integrating cross-view insights over time.

Implications and Future Directions

The MFN model provides a robust framework for multi-view sequential learning tasks, demonstrating enhanced accuracy and efficiency. By distinctly addressing both view-specific and cross-view interactions, MFN contributes to a deeper understanding of temporal data from diverse perspectives.

Looking forward, this research opens avenues for expanding MFN’s application across broader AI contexts, notably those involving complex, multi-source temporal data. Extending the model's capabilities to handle even more divergent views or dynamic changes in view availability could further enhance its utility.

In summary, the Memory Fusion Network provides a sophisticated, well-rounded approach to multi-view sequential learning, establishing new standards of efficacy and paving the way for future innovations in the field.