Direct Answer Sentence Transformer models might give low similarity scores to paraphrases due to three main factors: training data limitations, differences in semantic focus, and tokenization/context handling. These models are trained to capture semantic meaning, but their performance depends heavily on the specific data and objectives they were optimized for. If the training data didn’t include enough paraphrased examples or emphasized other tasks (like clustering dissimilar sentences), the model might prioritize features that don’t align with human intuition about paraphrase similarity. For example, a model trained on question-answer pairs might prioritize intent over surface-level similarity, causing paraphrases to appear less related.
Training and Semantic Nuance The model’s training objective plays a critical role. If the model was fine-tuned for a task like Natural Language Inference (NLI), which classifies sentences as “entailment,” “neutral,” or “contradiction,” it might focus on distinguishing subtle differences rather than measuring similarity. For instance, the sentences “The event was canceled due to rain” and “Heavy rainfall led to the event’s cancellation” are paraphrases, but the model might emphasize contextual cues (e.g., “due to” vs. “led to”) or syntactic structures, lowering the similarity score. Additionally, if the training data lacked diverse paraphrases, the model might not generalize well to unseen phrasing variations, especially if the paraphrases involve rare synonyms or complex reordering.
Tokenization and Context Sensitivity Sentence Transformers use tokenizers that split text into subwords or words, and small differences in phrasing can lead to divergent token patterns. For example, “quickly running” vs. “running fast” might use different token sequences, causing the model to treat them as less similar despite their equivalent meaning. Furthermore, these models analyze entire sentences holistically, so minor changes in word order or emphasis (e.g., “She primarily works remotely” vs. “She works remotely most of the time”) can alter the contextual embeddings. To improve results, consider fine-tuning the model on a paraphrase dataset (e.g., PAWS or MRPC) or using a similarity-focused objective like cosine similarity loss, which explicitly trains the model to map paraphrases closer in the embedding space.