If a Sentence Transformer model isn't capturing nuances like negation or sarcasm, the primary issue is likely insufficient exposure to such patterns during training. Here’s how to address this:
1. Fine-Tune with Domain-Specific Data The most effective approach is to fine-tune the model on data rich in the target nuances. For example, create a dataset with sentence pairs that contrast negated and non-negated statements (e.g., "The movie is good" vs. "The movie is not good") or labeled sarcastic phrases (e.g., "Great, another meeting!" with a sarcasm label). Use a contrastive loss function like CosineSimilarityLoss to train the model to distinguish between these cases. This forces the model to learn subtle differences. If labeled data is scarce, leverage existing datasets like the Stanford Sentiment Treebank (for negation) or sarcasm-detection datasets from platforms like Reddit or Twitter. Fine-tuning with even a few thousand targeted examples can significantly improve performance.
2. Modify Input Representations or Architecture Adjust preprocessing to highlight critical words. For negation, append markers like "NOT_" to negated terms (e.g., "not_happy") to make the model explicitly aware. For sarcasm, add context (e.g., metadata like user history or emojis) to the input. Architecturally, consider replacing the base transformer with a model pretrained on conversational data (e.g., BERT trained on dialogues or RoBERTa-Twitter). Alternatively, add task-specific layers: a bidirectional LSTM on top of embeddings to capture contextual dependencies, or an attention layer that weights negation keywords (e.g., "not," "never") more heavily during encoding.
3. Post-Processing and Hybrid Approaches Combine the Sentence Transformer with a dedicated classifier for the missing nuance. For example, use a sarcasm detector to flag sentences, then adjust their embeddings by concatenating the sarcasm score or flipping their semantic direction. For negation, train a binary classifier to detect negated phrases and modify embeddings (e.g., subtract a "negation vector" learned from contrastive examples). This avoids retraining the entire model and allows incremental improvements. Additionally, use evaluation metrics tailored to the nuance—e.g., measure embedding similarity between "He is not happy" and "He is sad" versus "He is happy" to test negation handling.
Trade-offs and Considerations Fine-tuning requires labeled data but offers the most direct improvement. Architectural changes demand more computational resources, while hybrid approaches add pipeline complexity. Start with fine-tuning on targeted data, and if nuances remain, explore augmenting inputs or adding post-processing classifiers. Validate using task-specific benchmarks (e.g., semantic textual similarity tests with negated pairs) to quantify progress.