Yes, Sentence Transformers can be used in machine translation workflows to find sentence alignments between languages. Sentence Transformers generate dense vector representations (embeddings) that capture semantic meaning, which allows them to identify pairs of sentences in different languages that convey the same meaning. This is particularly useful for aligning sentences in parallel corpora, a critical step in training or fine-tuning machine translation models. By leveraging multilingual Sentence Transformers—models trained to align embeddings across languages—developers can map sentences from different languages into a shared semantic space, enabling efficient similarity-based matching.
For example, a multilingual model like paraphrase-multilingual-MiniLM-L12-v2 encodes sentences in multiple languages into a vector space where translations are positioned close to one another. To align sentences, you would:
- Encode source and target language sentences into embeddings.
- Compute cosine similarity between source and target embeddings.
- Select pairs with the highest similarity scores as probable translations. This approach works well for semantically similar sentences, even when they differ in structure or wording (e.g., idiomatic phrases). It outperforms traditional methods like dictionary-based alignment in cases where direct word-for-word matching fails. However, its effectiveness depends on the quality of the multilingual embeddings, which requires the model to have been trained on diverse, high-quality parallel data for the target language pair.
There are limitations. First, performance may degrade for language pairs or domains not well-represented in the model’s training data. Second, aligning long documents with complex sentence splits (e.g., one sentence in English mapping to two in Spanish) might require additional heuristics. Finally, while Sentence Transformers are efficient, processing large corpora can still be computationally intensive. Despite these challenges, integrating Sentence Transformers into alignment pipelines offers a robust, semantics-driven alternative to traditional methods, especially when paired with preprocessing (e.g., filtering) and postprocessing (e.g., validation) steps to refine results.