Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis (1911.05544v2)

Abstract: Multimodal language analysis often considers relationships between features based on text and those based on acoustical and visual properties. Text features typically outperform non-text features in sentiment analysis or emotion recognition tasks in part because the text features are derived from advanced LLMs or word embeddings trained on massive data sources while audio and video features are human-engineered and comparatively underdeveloped. Given that the text, audio, and video are describing the same utterance in different ways, we hypothesize that the multimodal sentiment analysis and emotion recognition can be improved by learning (hidden) correlations between features extracted from the outer product of text and audio (we call this text-based audio) and analogous text-based video. This paper proposes a novel model, the Interaction Canonical Correlation Network (ICCN), to learn such multimodal embeddings. ICCN learns correlations between all three modes via deep canonical correlation analysis (DCCA) and the proposed embeddings are then tested on several benchmark datasets and against other state-of-the-art multimodal embedding algorithms. Empirical results and ablation studies confirm the effectiveness of ICCN in capturing useful information from all three views.

• The paper introduces ICCN, a novel model that uses outer-product-based DCCA to capture interactions among text, audio, and video for improved sentiment analysis.
• The model leverages CNN layers to extract feature representations that maximize intermodal correlations, outperforming state-of-the-art methods on benchmark datasets.
• The findings highlight the potential of integrating multimodal interactions to enhance emotion recognition and guide future research in AI interpretability.

Analyzing Hidden Multimodal Correlations for Sentiment Analysis

The research paper "Learning Hidden Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Sentiment Analysis" by Sun et al. addresses the challenge of multimodal sentiment analysis by proposing a novel model named Interaction Canonical Correlation Network (ICCN). Their approach incorporates deep canonical correlation analysis (DCCA) to effectively learn from the interactions between text, audio, and video modalities.

Problem Context and Hypothesis

Multimodal sentiment analysis requires processing and integrating information from text, audio, and visual data. Traditionally, text-centric models outperform their audio and visual counterparts due to mature text representation techniques and the inherent richness of linguistic data for sentiment tasks. The underrepresentation of audio and visual features in existing methods can lead to suboptimal performance in sentiment analysis. This paper hypothesizes that enhancing the intermodal relationship between text and non-text data could improve multimodal sentiment analysis outcomes.

Core Methodology: Interaction Canonical Correlation Network

The paper introduces ICCN, which exploits the outer-product between text features and audio/video features to capture their interactions. By applying DCCA, ICCN learns a subspace where the correlation between text-based audio and text-based video is maximized. This approach moves beyond simple concatenations of unimodal features by addressing hidden correlations that can contribute to improved performance in sentiment analysis and emotion recognition tasks.

The ICCN architecture employs convolutional neural network (CNN) layers to extract features from the outer-product matrices of text-audio and text-video interactions. The output from these CNNs, representing text-based audio and video, is fed into DCCA layers for optimizing correlations. After training, ICCN produces a multimodal embedding that integrates text, audio, and video features, enabling improved classification performance on downstream tasks.

Empirical Evaluation and Comparison

The ICCN model was evaluated against state-of-the-art techniques such as Tensor Fusion Network (TFN), Low-Rank Multimodal Fusion (LMF), and Multimodal Factorization Model (MFM), utilizing benchmark datasets CMU-MOSI, CMU-MOSEI, and IEMOCAP. ICCN consistently demonstrated superior performance, particularly in tasks involving complex human emotions and sentiments, suggesting that the learned interactions significantly enrich multimodal representations.

Speculative Future Implications and Challenges

The ICCN model marks a noteworthy stride in leveraging multimodal data for sentiment analysis. Future research may consider incorporating dynamic intra-actions within each modality, trading off between maximizing canonical correlation and enhancing end-task performance, and developing interpretable models for multimodal interaction. The continual refinement of such techniques is crucial in progressing towards more generalizable AI capable of nuanced understanding and interaction with multimodal data streams in real-world scenarios.

Conclusion

Sun et al.'s work presents ICCN as an effective solution for multimodal sentiment analysis by utilizing deep canonical correlation via outer-product-based representations. The empirical results highlight its proficiency relative to traditional methods and underline the potential of exploring multifaceted interactions between modalities. As such, this research paves the way for advanced multimodal systems that offer richer and more accurate insights into human affective states.