How can you address token misalignment when using pre-trained models for machine translation

Question

Can you tell me How can you address token misalignment when using pre-trained models for machine translation?

Answer 1

Token misalignment in machine translation can be addressed by using the same tokenizer as the pre-trained model, ensuring consistent encoding-decoding, and aligning wordpiece or subword tokens properly.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses the same pre-trained tokenizer and model to maintain encoding-decoding consistency.
• Handles padding and truncation to avoid token misalignment.
• Ensures subword and wordpiece tokens stay properly aligned with the source text.

Hence, consistent use of pre-trained model-specific tokenizers and careful encoding-decoding practices prevent token misalignment, leading to accurate and coherent translations.