Yes, Sentence Transformers can effectively identify semantically similar content in content moderation, even when harmful messages are rephrased. These models generate dense vector representations (embeddings) of text, capturing semantic meaning rather than relying on exact keyword matches. By comparing the similarity of these embeddings, platforms can detect variations of harmful content that avoid traditional keyword-based detection methods.
Sentence Transformers, such as models based on BERT or RoBERTa architectures, are fine-tuned to optimize for semantic similarity. For example, a model trained on pairs of paraphrased sentences learns to map phrases like "You should hurt yourself" and "Self-harm might be an option" to vectors that are close in the embedding space. This allows moderators to flag content that shares intent with known harmful examples, even if the wording differs. Practical implementations often use cosine similarity to measure how closely a new message aligns with a predefined set of banned content embeddings. For instance, a platform could precompute embeddings for prohibited phrases (e.g., hate speech templates) and compare user-generated content against them in real time.
However, challenges exist. The model’s effectiveness depends on the quality and diversity of its training data. If harmful content uses slang, code words, or niche terminology not present during training, the model may miss these variations. Additionally, semantic similarity alone can’t always capture context—for example, sarcasm or reclaimed language might be misinterpreted. To mitigate this, many systems combine Sentence Transformers with additional checks, such as user reporting or human review pipelines, to reduce false positives. Overall, while not perfect, Sentence Transformers provide a scalable way to handle evolving abusive content that simple keyword filters cannot address.