Two paraphrased sentences might not receive a high similarity score from a Sentence Transformer model due to differences in how the model represents semantic meaning, limitations in training data, or variations in sentence structure. Sentence Transformers map text to dense vector embeddings, and similarity scores depend on how well the model captures the relationship between the sentences. Here are three key reasons this can happen:
1. Training Data and Model Objective Sentence Transformer models are typically trained on specific tasks (e.g., semantic textual similarity, natural language inference) using datasets that define what "similarity" means in their context. For example, if the model was trained on Natural Language Inference (NLI) data, it might prioritize distinguishing between contradiction, entailment, and neutrality rather than fine-grained paraphrase detection. If your paraphrases involve subtle rephrasing not well-represented in the training data, the model may not recognize them as highly similar. For instance, the model might treat "The event was canceled due to rain" and "Rain caused the event’s cancellation" as moderately similar but not identical, depending on how such examples were weighted during training.
2. Sentence Structure and Word Choice Paraphrases often involve changes in syntax, vocabulary, or emphasis that the model may interpret as meaningful differences. For example, "The quick brown fox jumps over the lazy dog" and "A fast, dark-colored fox leaps above an idle canine" might be paraphrases, but the model could focus on the shift from "quick" to "fast" (synonyms) versus "lazy" to "idle" (similar but distinct connotations). Additionally, models like Sentence-BERT use pooling strategies (e.g., mean pooling of token embeddings) that might dilute subtle semantic relationships if sentence structures differ significantly.
3. Embedding Space Limitations The model’s embedding space might not perfectly align with human intuition for paraphrase similarity. For instance, domain-specific terms or rare phrasings in one sentence could place its embedding in a different region of the vector space, even if the overall meaning is equivalent. A model trained on general text might struggle with specialized paraphrases (e.g., "myocardial infarction" vs. "heart attack" in a medical context) if those terms weren’t frequent in its training data. Additionally, the choice of similarity metric (e.g., cosine similarity) might not fully capture nuanced relationships, especially if the embeddings are normalized or scaled in a way that reduces sensitivity to certain semantic features.
What You Can Do
To improve results, try fine-tuning the model on a paraphrase dataset (e.g., MRPC or PAWS) to better align its embeddings with your definition of similarity. Alternatively, test different pre-trained models (e.g., paraphrase-MiniLM-L6-v2 is optimized for paraphrase detection) or adjust the similarity threshold for your use case. If the sentences are domain-specific, ensure the model was trained on relevant data or augment it with custom examples.