Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis (2107.13669v2)

Abstract: Multimodal sentiment analysis aims to extract and integrate semantic information collected from multiple modalities to recognize the expressed emotions and sentiment in multimodal data. This research area's major concern lies in developing an extraordinary fusion scheme that can extract and integrate key information from various modalities. However, one issue that may restrict previous work to achieve a higher level is the lack of proper modeling for the dynamics of the competition between the independence and relevance among modalities, which could deteriorate fusion outcomes by causing the collapse of modality-specific feature space or introducing extra noise. To mitigate this, we propose the Bi-Bimodal Fusion Network (BBFN), a novel end-to-end network that performs fusion (relevance increment) and separation (difference increment) on pairwise modality representations. The two parts are trained simultaneously such that the combat between them is simulated. The model takes two bimodal pairs as input due to the known information imbalance among modalities. In addition, we leverage a gated control mechanism in the Transformer architecture to further improve the final output. Experimental results on three datasets (CMU-MOSI, CMU-MOSEI, and UR-FUNNY) verifies that our model significantly outperforms the SOTA. The implementation of this work is available at https://github.com/declare-lab/multimodal-deep-learning.

Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis

The paper "Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis" presents an innovative approach to handling the challenges associated with multimodal sentiment analysis (MSA) by introducing the Bi-Bimodal Fusion Network (BBFN). This paper seeks to address limitations in previous work by incorporating a novel fusion scheme that enhances performance by dynamically balancing independence and correlation among modalities in the fusion process.

Methodological Contributions

The BBFN introduces a distinct fusion approach, focusing on pairwise modality interactions rather than traditional ternary combinations, which often led to information imbalance. By selecting text-focused modality pairs—specifically text-visual (TV) and text-acoustic (TA)—the BBFN maximizes the relevance of the text modality, which empirical studies have shown to be the most informative in MSA tasks. The BBFN iteratively enhances feature integration between modality pairs, supported by a gated control mechanism integrated into a Transformer-based architecture, augmenting the output effectiveness.

To prevent "feature space collapse," where representation vectors converge undesirably during fusion, the paper introduces the layer-wise feature space separator. This mechanism preserves the intrinsic independence between modalities, ensuring that each retains unique statistical properties throughout the fusion layers.

Experimental Results

Quantitative evaluation on datasets such as CMU-MOSI, CMU-MOSEI, and UR-FUNNY demonstrated that BBFN surpasses existing state-of-the-art (SOTA) models in most metrics. Notably, on CMU-MOSEI, BBFN achieved significant improvements in binary classification and mean absolute error, with over 4% enhancement in the latter. These robust results validate the efficacy of the BBFN in handling complex multimodal data.

Analysis and Implications

The comprehensive evaluation includes an ablation paper that examines the influence of BBFN's components, reaffirming the pivotal role of gating mechanisms and feature separation in enhancing model performance. The authors explore variations where different modality combinations are tested, providing evidence of the architectural versatility and the necessity of specialization in combining modalities.

There is a balanced assessment of gating weights, which suggests that BBFN can control information flow adaptively, aligning with the relative importance of each input modality's contribution. This dynamic approach suggests broader applicability and adaptability across various contexts of multimodal interaction beyond sentiment analysis.

Conclusions and Future Directions

BBFN offers a structured methodology for addressing the nuanced needs of multimodal sentiment analysis, offering enhancements in both practical application and theoretical understanding of modality interactions. The integration of specific mechanisms such as the feature separator and gated control opens avenues for further exploration in tasks requiring multi-faceted data interpretation.

Future research may focus on extending these methodologies to other multimodal tasks beyond sentiment analysis, optimizing the trade-offs between information richness and redundancy. Additional exploration into task-specific fusion strategies and their coordination with task-solving modules will likely yield further advancements in AI-driven fusion technologies.