Direct Answer
Sentence Transformers can detect duplicate questions in forums by converting text into numerical vectors (embeddings) and measuring their similarity. A typical approach uses a pre-trained model like all-MiniLM-L6-v2 to encode questions into dense vectors. For example, if a user posts “How to reset a router?” and another asks "What’s the way to reboot my network device?", both sentences are fed into the model to generate 384-dimensional vectors. The cosine similarity between these vectors is then calculated—a value close to 1 indicates near-identical meaning. Developers can set a threshold (e.g., 0.85) to flag potential duplicates automatically. This method works across languages and phrasings without relying on exact keyword matches.
Implementation Example
To implement this, start by installing the sentence-transformers library. Preprocess input text by lowercasing and removing special characters. Load the model and encode all existing forum questions into embeddings, storing them in a database or vector index like FAISS for efficient lookup. When a new question is posted, encode it and compare it against stored embeddings using cosine similarity. For instance:
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('all-MiniLM-L6-v2')
existing_questions = ["How to reset a router?", "..."]
new_question = "What's the way to reboot my network device?"
# Encode
existing_embeddings = model.encode(existing_questions)
new_embedding = model.encode([new_question])
# Compare
similarities = util.cos_sim(new_embedding, existing_embeddings)[0]
duplicates = [i for i, score in enumerate(similarities) if score > 0.85]
Tools like FAISS or Annoy accelerate similarity searches in large datasets, making this scalable for platforms with millions of questions.
Challenges and Optimizations Key challenges include handling typos (“reseting router”) and domain-specific jargon. Preprocessing steps like lemmatization (e.g., converting “resetting” to “reset”) improve consistency. For niche forums (e.g., medical Q&A), fine-tuning the model on domain-specific data (using datasets like Quora question pairs) boosts accuracy. Threshold tuning is critical: too low causes false positives, too high misses valid duplicates. A/B testing with real user feedback helps balance precision and recall. Additionally, caching frequently asked questions and using approximate nearest neighbor libraries reduce latency. This approach balances accuracy with computational efficiency, making it practical for real-time duplicate detection.