Ways to improve the accuracy of specialized terminology translations
Hibiki's base model is better optimized for general-purpose language scenarios, but may not be accurate enough for specialized terminology. The following solution can significantly improve it:
- Create a customized thesaurus: Use the MADLAD system to construct domain-specific bilingual concordance tables that enforce alignment relationships for specific phrases.
- fine-tuned model: Continue training for 10-20 epochs on the target domain data, focusing on term-dense samples.
- post-processing replacement: After obtaining the translated text, the rule engine is used to replace key terms.
- contextual enhancement: Add domain cue words before speech input to help the model better understand the context.
- Human intervention correction: Implement human-in-the-loop mechanism to record mistranslation samples for feedback to the system.
Hibiki's synthetic data generation technology is particularly well suited for extending specialized domain data. By collecting a small number of real conversations (e.g., 20 hours of medical consultations), more than 100 hours of augmented training data can be generated. This approach has been shown to improve terminology accuracy by 401 TP3T in experiments in finance and law.
This answer comes from the articleHibiki: a real-time speech translation model, streaming translation that preserves the characteristics of the original voiceThe
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