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To achieve effectiveness in multi-lingual and cross-lingual applications like machine translation, pre-trained encoder-decoder models require training on multi-lingual data. This process necessitates a vocabulary containing tokens from all involved languages, which allows the model to learn shared representations and develop capabilities for both understanding and generation across different languages.

Multi-lingual Pre-training for Encoder-Decoder Models

A company is developing a single sequence-to-sequence model to handle customer service requests in both English and Spanish. They are considering two pre-training strategies. Evaluate the two strategies below and determine which is more likely to result in a single, effective bilingual model. Justify your choice based on the principles of training such models.

Evaluating Pre-training Strategies for a Bilingual Model

A developer is pre-training a single sequence-to-sequence model for translating between Spanish and Portuguese. They observe that the model struggles to find common patterns between the two languages, treating words like 'sol' (Spanish) and 'sol' (Portuguese), which both mean 'sun', as completely unrelated concepts. Explain why creating a unified vocabulary containing tokens from both languages is a critical step to resolve this issue and enable the model to learn shared representations.

Diagnosing Cross-Lingual Representation Issues

A team is building a single encoder-decoder model intended to translate between Japanese, Korean, and Mandarin. They pre-train the model on a large, combined corpus of all three languages. However, instead of creating a unified vocabulary that includes tokens from all three languages, they use three separate, language-specific vocabularies. What is the most direct and critical consequence of this design choice on the model's translation performance?

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