Sentence Transformers, while effective for many NLP tasks, face several challenges in accurately capturing sentence meaning. One key limitation is their sensitivity to contextual nuances and ambiguity. These models generate embeddings based on statistical patterns in training data, which can struggle with sentences that rely on implicit context, sarcasm, or domain-specific jargon. For example, the word "bank" could refer to a financial institution or a riverbank, but the model might not consistently infer the correct meaning without clear contextual clues in the sentence. Similarly, idiomatic expressions like "break a leg" might be misinterpreted as literal rather than figurative. This issue stems from the fact that Sentence Transformers often prioritize surface-level semantic similarity over deeper pragmatic understanding, leading to embeddings that don’t fully reflect intended meaning.
Another challenge is domain adaptation and data bias. Sentence Transformers are typically pretrained on general-purpose corpora (e.g., Wikipedia or web-crawled text), which may not align with specialized domains like legal documents or medical jargon. For instance, a model trained on generic text might fail to distinguish between "cell" (biology) and "cell" (mobile phone) in a biomedical context. Additionally, biases in training data can propagate into embeddings. For example, gender stereotypes in language (e.g., associating "nurse" with feminine pronouns) might skew similarity scores between sentences. Developers often need to fine-tune models on domain-specific data to mitigate this, but this requires additional labeled data and computational resources, which isn’t always feasible.
Finally, structural complexity and sentence length pose challenges. Sentence Transformers use fixed-length vector representations, which can struggle to capture intricate relationships in long or syntactically complex sentences. For example, a sentence with nested clauses or multiple negations (e.g., "The decision wasn’t entirely unjustified, though not wholly correct either") might lose nuanced meaning when compressed into a single vector. Additionally, models like BERT-based architectures have token limits (e.g., 512 tokens), truncating or splitting longer texts. This limitation forces developers to preprocess inputs (e.g., chunking text), which can disrupt coherence and degrade embedding quality. While techniques like pooling (e.g., mean-pooling tokens) help, they often sacrifice positional or dependency information critical for precise semantic representation.