MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis (2005.03545v3)

Abstract: Multimodal Sentiment Analysis is an active area of research that leverages multimodal signals for affective understanding of user-generated videos. The predominant approach, addressing this task, has been to develop sophisticated fusion techniques. However, the heterogeneous nature of the signals creates distributional modality gaps that pose significant challenges. In this paper, we aim to learn effective modality representations to aid the process of fusion. We propose a novel framework, MISA, which projects each modality to two distinct subspaces. The first subspace is modality-invariant, where the representations across modalities learn their commonalities and reduce the modality gap. The second subspace is modality-specific, which is private to each modality and captures their characteristic features. These representations provide a holistic view of the multimodal data, which is used for fusion that leads to task predictions. Our experiments on popular sentiment analysis benchmarks, MOSI and MOSEI, demonstrate significant gains over state-of-the-art models. We also consider the task of Multimodal Humor Detection and experiment on the recently proposed UR_FUNNY dataset. Here too, our model fares better than strong baselines, establishing MISA as a useful multimodal framework.

Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis

The paper "MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis" addresses a significant challenge in Multimodal Sentiment Analysis (MSA): the need to bridge the distributional gaps between heterogeneous modality signals in user-generated videos. The authors propose MISA, a novel framework designed to enhance the multimodal representation learning process preceding fusion. By focusing on disentangled subspace creation for each modality, MISA provides both a modality-invariant and modality-specific view of data, which more effectively supports the fusion process leading to sentiment prediction.

Methodology

MISA introduces two distinct subspaces for each modality: modality-invariant and modality-specific. The modality-invariant subspace aims to reduce distributional gaps by aligning features across different modalities. Conversely, the modality-specific subspace captures unique characteristics inherent to each modality. These subspace representations, facilitated by a combination of distributional similarity, orthogonal regularization, and reconstruction loss functions, are then consolidated to form a comprehensive fusion basis.

The framework leverages a Transformer-based multi-head self-attention mechanism to combine these subspace representations into a joint vector. This approach enhances each subspace's representation by incorporating and learning from the synergistic potential of cross-modal interactions.

Results and Key Findings

The paper reports substantial improvements in MSA benchmarks, demonstrating MISA's efficacy against state-of-the-art models. Specifically, MISA shows significant performance boosts on the MOSI and MOSEI datasets, as well as in Multimodal Humor Detection on the UR_FUNNY dataset. The employment of both modality-invariant and modality-specific subspaces in the representation-learning phase precedes the successful fusion step, which is critical for the model's performance gains.

Implications and Future Directions

This research significantly contributes to the theoretical understanding of multimodal data representation. By verifying through experimental results that the strategic factorization of data into invariant and specific domains can facilitate superior fusion outcomes, MISA challenges the traditional emphasis on sophisticated fusion mechanisms alone. It posits a shift toward enhancing the quality of input representations as a crucial precursor to effective multimodal fusion.

Practically, MISA's applicability in sentiment analysis tasks offers pathways for improved affective computing in diverse applications such as emotion recognition, human-computer interaction, and socially aware AI systems. Future developments may explore adapting the MISA framework to more complex datasets and other multimodal tasks beyond sentiment and humor detection, such as emotion categorization and intent understanding.

Overall, the MISA framework presents a compelling case for a more nuanced approach to modality representation learning. By distinguishing between shared and unique modality features, it fosters a deeper integration of multimodal data properties, setting the stage for enhanced predictive capabilities in multimodal tasks.