Using direct translation and meaning confirmation to reduce ambiguity in AI‑powered book translation.
At LectuLibre, we translate entire books using LLMs like Claude and DeepSeek. Early on, we noticed that literal or direct translations often missed the nuance of ambiguous phrases — a character’s nickname, a culturally specific idiom, or a word with multiple meanings. A single‑pass translation pipeline just wasn’t enough. That’s when we built 直接翻译与含义确认 (Direct Translation & Meaning Confirmation), a two‑stage process that first produces a draft translation, then re‑examines tricky bits with a meaning‑confirmation step. Here’s exactly how we did it, including the Python code, the trade‑offs, and the unexpected lessons.
The Problem: When Direct Translation Falls Short
Our initial pipeline was simple: split an EPUB into chunks, send each chunk to an LLM with a prompt like “Translate the following English text to Spanish”, and stitch the results. It worked for straightforward paragraphs, but books are full of traps:
Polysemous words: “He got the bat” could be the animal or sports equipment.
