Follow the White Rabbit: Using Embeddings So You Never Get Lost in Translation

The article explores an experiment using embedding models to evaluate the fidelity of translations by comparing English and French versions of "Alice's Adventures in Wonderland." Utilizing the Qwen3-Embedding-4B multilingual model, the study computes semantic similarities between chapters and paragraphs, revealing the accuracy of Henri Bué's 1865 French translation. The embedding model translates text into vectors, allowing for cosine similarity computation to assess semantic closeness. The experiment highlights the utility of embedding models for translation quality assurance and cross-lingual document alignment, demonstrating a sophisticated method for matching and comparing texts across languages. The study emphasizes the practicality of using a single multilingual model for various language pairs, offering a new approach to translation QA and multilingual corpus analysis.

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