To address misaligned embeddings for similar sentences across languages in a multilingual model, start by diagnosing the root cause. Multilingual models like mBERT or XLM-R rely on shared embedding spaces trained on parallel data (e.g., translated sentences). If sentences in different languages aren’t close, it often indicates insufficient exposure to aligned examples during training, domain mismatch, or structural differences between languages. For example, idiomatic phrases like “raining cats and dogs” (English) versus “raining ropes” (German: “es regnet Bindfäden”) might not align well if the model hasn’t seen enough parallel idioms.
Step 1: Improve Training Data and Fine-Tuning Fine-tune the model on domain-specific or language-specific parallel data. For instance, if you’re working with legal texts, gather translated legal documents and retrain the model on them. Use techniques like translation ranking loss, which explicitly forces the model to map translated pairs closer in the embedding space. If parallel data is scarce, leverage unsupervised methods like iterative back-translation: generate synthetic parallel sentences by translating monolingual data back and forth between languages. Tools like OPUS or the Tatoeba corpus can provide additional aligned sentence pairs for common languages.
Step 2: Adjust Model Architecture or Training Strategy Modify the model’s alignment mechanism. For example, add a language-adversarial loss to reduce language-specific biases in the embeddings, ensuring the model focuses on semantic similarity rather than language identity. Alternatively, use language-specific tokenization (e.g., sentencepiece models tailored for each language) to reduce noise from subword mismatches. If the issue persists, consider using a model with stronger cross-lingual capabilities, such as LaBSE (Language-agnostic BERT Sentence Embedding), which is explicitly optimized for multilingual alignment through dual-encoder training.
Step 3: Post-Processing and Evaluation Apply post-hoc alignment techniques. For example, use Procrustes analysis (a linear transformation) to map embeddings of one language to another using a small set of aligned sentence pairs. Normalize embeddings to a unit sphere to ensure cosine similarity comparisons are stable. Validate the results using benchmarks like XNLI (Cross-lingual Natural Language Inference) or Tatoeba retrieval tasks. If performance remains uneven, consider hybrid approaches: translate non-aligned sentences to a pivot language (e.g., English) and compare embeddings in that space, though this adds latency and depends on translation quality.
By combining targeted fine-tuning, architectural adjustments, and post-processing, you can improve cross-lingual alignment while maintaining computational efficiency. Always validate with downstream tasks (e.g., retrieval or classification) to ensure the changes address the specific use case.