What are the challenges and solutions for data tokenization in multi-lingual generative models

Question

Can you name the challenges and solutions for data tokenization in multi-lingual generative models?

Answer 1

Challenges and solutions for data tokenization in multi-lingual generative models are as follows:

Challenges in Multi-lingual Tokenization:

• Vocabulary Size: Handling large vocabularies for diverse languages leads to memory and efficiency issues.
• Rare Tokens: Languages with fewer training examples produce many out-of-vocabulary (OOV) tokens.
• Script Variability: Different scripts (e.g., Latin vs. Cyrillic) require flexible tokenization strategies.
• Consistency: Tokenization inconsistencies across languages impact model performance.

Solutions for that:

• Subword Tokenization: It uses algorithms like Byte Pair Encoding (BPE) or SentencePiece to generate subword units shared across languages.
• Shared Vocabulary: Train a common vocabulary to leverage cross-lingual transfer.
• Language Tags: It Adds language-specific tokens (e.g., <en> for English) to guide the model.

The outcome of the above code would be that subword tokenization handles OOV words efficiently, and shared vocabulary supports cross-lingual understanding.