Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Machine Learning

Background:

• Human language relies on multiple information sources (modalities) like tone, facial expressions, and speech.
• Current multimodal learning models excel in tasks like sentiment analysis but often lack interpretability, acting as black boxes.

Purpose of the Study:

• To develop a novel method, Multimodal Routing, for enhancing the interpretability of multimodal learning systems.
• To dynamically adjust the influence of different input modalities based on individual data samples.

Main Methods:

• Propose Multimodal Routing, a technique that assigns dynamic weights to input modalities and output representations.
• The routing mechanism identifies the importance of individual modalities and cross-modality interactions.

Main Results:

• Multimodal Routing provides interpretable insights into modality-prediction relationships.
• Interpretations are available globally (dataset-wide trends) and locally (per-sample analysis).
• The method achieves performance competitive with state-of-the-art approaches.

Conclusions:

• Multimodal Routing offers a significant advancement in understanding how different communication modalities contribute to predictions.
• This approach enhances transparency in complex AI systems, enabling more reliable human-centric task performance.