If I find that minor differences in sentences (like punctuation or letter casing) result in big changes in similarity scores, how can I make the model more robust to these variations?

To make your model more robust to minor sentence variations like punctuation or casing, focus on three key areas: preprocessing, model architecture choices, and post-processing. First, normalize inputs by standardizing text formatting before analysis. Second, use models that inherently handle small variations through their design. Third, implement thresholds or error margins in similarity scoring to account for noise.

Start with preprocessing to reduce irrelevant differences. Convert all text to lowercase using .lower() in Python, and remove punctuation using regular expressions or libraries like string.punctuation. For example, transform both “Hello, World!” and “hello world” into “hello world” before processing. Consider using tokenizers that ignore stopwords or apply lemmatization (e.g., spaCy's lemmatizer) to group similar words like “running” and “ran.” For embeddings, tools like sentence-transformers allow configuring tokenization rules to ignore case during text encoding. These steps create a consistent baseline for comparison.

Next, choose models that prioritize semantic meaning over surface-level differences. Models like BERT or Universal Sentence Encoder (USE) generate embeddings that capture contextual relationships, making them less sensitive to minor syntactic changes. For example, the cosine similarity between “The cat sat” and “the cat sat.” should remain high with these models. If using custom models, add noise to training data by randomly altering casing/punctuation in examples. This teaches the model to treat variations as equivalent. For rule-based systems, implement fuzzy matching with edit-distance thresholds (e.g., allowing 1-2 character differences) using libraries like fuzzywuzzy.

Finally, adjust similarity scoring logic. Instead of treating scores as absolute, define a tolerance range. For instance, consider scores within 0.95-1.0 as identical, and 0.85-0.95 as near-matches. Use dynamic thresholds based on text length—shorter texts may need tighter margins. For critical applications, add a validation layer: if two texts score 0.8 similarity, check if differences are only punctuation/casing before finalizing the result. Tools like textdistance provide multiple similarity metrics (Jaccard, Levenshtein) that can be combined for consensus. Test these changes systematically by creating benchmark pairs with controlled variations to measure improvement.