Content Moderation: Model Architecture
Explore how to design an effective content moderation model architecture that integrates text, image, video, and audio signals. Understand per-modality encoders, fusion strategies, and tiered ensemble approaches to balance latency and accuracy. Gain insights into building calibrated confidence scores and an active learning loop for continuous model improvement.
In the previous lesson, we established a data strategy for content moderation that defined multimodal signal extraction, policy-aware labeling, and an active learning loop. Now we shift from what data the system consumes to how the system processes it. The core interview question is direct: how would you design a model architecture that classifies content across text, image, video, and audio at platform scale while balancing latency, accuracy, and cost?
This lesson covers three architectural pillars. First, per-modality encoders produce fixed-dimension embeddings from each input type. Second, a fusion layer combines those modality signals into a unified representation. Third, a tiered ensemble separates fast automated triage from slow deep classification on borderline content. A monolithic single-model approach fails here because category-specific nuances, such as hate speech in memes vs. graphic violence in video, demand specialized components. The architecture must also output well-calibrated confidence scores, because those scores power both the routing logic between tiers and the active learning flywheel that continuously improves the model.
Per-modality encoders
Each modality requires a dedicated encoder that transforms raw input into a fixed-dimension embedding vector. Think of each encoder as a specialist translator: it reads one “language” (pixels, waveforms, tokens) and produces a standardized numerical summary that downstream components can compare and combine.
The following encoders form the foundation of the pipeline:
Text encoder: A fine-tuned transformer such as multilingual BERT or XLM-R processes captions, comments, and overlaid text extracted via OCR, producing a 768-dimensional embedding that captures semantic meaning across languages.
Image encoder: A vision transformer or EfficientNet processes visual content, learning to detect hate symbols, nudity, and graphic violence and compressing those signals into a 512-dimensional embedding.
Video encoder: A temporal model such as SlowFast or TimeSformer processes sampled keyframes to capture escalation patterns, like a conversation turning violent, that are invisible in any single frame, outputting a 512-dimensional embedding.
Audio encoder: A model like Whisper produces transcription embeddings while a parallel acoustic feature extractor captures ...