Answer: Sentence Transformers can detect duplicate questions by converting text into numerical vectors (embeddings) and measuring their similarity. This is useful in forums or Q&A platforms to reduce redundancy and direct users to existing answers. Here’s a step-by-step example of how it works:
Step 1: Model Selection and Embedding Generation
First, choose a pre-trained Sentence Transformer model like all-mpnet-base-v2, which is optimized for semantic similarity. For every question in the forum, generate an embedding—a high-dimensional vector representing the question’s meaning. For example, the question “How to fix a slow internet connection?” and its paraphrase “What steps can I take to resolve sluggish Wi-Fi speeds?” would both be converted into embeddings. These embeddings are stored in a database (e.g., using FAISS or Annoy for efficient lookup).
Step 2: Similarity Comparison for New Questions When a user submits a new question, generate its embedding using the same model. Compare this embedding against all stored embeddings using cosine similarity. For instance, if the new question is “How do I troubleshoot slow network speeds?”, the model would calculate its similarity to existing embeddings. If the similarity score exceeds a threshold (e.g., 0.85), the system flags it as a potential duplicate. This threshold is tuned based on domain-specific testing to balance precision (avoiding false duplicates) and recall (catching true duplicates).
Practical Implementation and Tools
To scale this, use libraries like sentence-transformers for embedding generation and FAISS for fast nearest-neighbor searches. For example, a Python script could:
- Encode all existing questions into embeddings and index them with FAISS.
- For a new question, compute its embedding and query the FAISS index for the top-5 most similar entries.
- Apply a threshold to filter results and display potential duplicates.
Challenges include handling short or ambiguous questions (e.g., “PC not turning on” vs. “Laptop won’t boot”) and computational efficiency. Tools like FAISS mitigate performance issues, while domain-specific fine-tuning (e.g., training on tech support data) improves accuracy for niche forums. This approach ensures users find answers faster while reducing moderation overhead.