How can I address a scenario where similar sentences in different languages are not close in embedding space when using a multilingual model?

To address scenarios where similar sentences in different languages aren’t close in embedding space with a multilingual model, you need to improve cross-lingual alignment during training, fine-tuning, or post-processing. Multilingual models like mBERT or XLM-R are trained to map semantically equivalent sentences from different languages into similar vectors, but this doesn’t always work perfectly due to differences in training data, language structure, or domain mismatch. Here’s how to tackle this issue effectively.

First, fine-tune the model with parallel data. If the model hasn’t been exposed to enough aligned sentences in your target languages during pre-training, embeddings may not align well. For example, if your task involves English and Japanese, gather a dataset of parallel sentences (e.g., translated product reviews) and fine-tune the model using a contrastive loss (like triplet loss) or a translation ranking objective. This forces the model to prioritize similarity between translations. For instance, using a contrastive loss, you can explicitly train the model to minimize the distance between “Hello, how are you?” (English) and “Hola, ¿cómo estás?” (Spanish) while maximizing the distance from unrelated sentences. Libraries like SentenceTransformers simplify this by providing tools for training with paired data.

Second, adjust tokenization and model architecture. Languages with different scripts or morphological structures (e.g., Chinese vs. German) may not share subword tokens effectively, leading to misaligned embeddings. Consider using a shared vocabulary across languages or a tokenizer designed for multilingual inputs. For example, replacing language-specific tokenizers with a unified SentencePiece model can help align subword units. Additionally, layer-wise adjustments—like freezing earlier layers and retraining higher layers on parallel data—can refine cross-lingual mappings without destabilizing the model’s core multilingual knowledge.

Finally, apply post-processing techniques. If retraining isn’t feasible, use methods like embedding alignment to project embeddings into a shared space. For example, use linear transformations (learned via Procrustes analysis) to map Japanese embeddings to the English space using a small parallel corpus. Tools like VecMap or the MUSE library automate this for word embeddings and can be adapted for sentence-level alignment. Another approach is to use multilingual sentence embeddings with built-in alignment, like LaBSE, which is explicitly trained for translation pairs. These steps ensure that even if the base model isn’t perfectly aligned, downstream applications can still leverage cross-lingual similarity.

By combining targeted fine-tuning, architectural adjustments, and post-processing, you can bridge the gap between languages in embedding space, enabling more robust cross-lingual applications like retrieval, classification, or translation.