Multilingual models in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why use multilingual models?

Which of the following is the main advantage of using a multilingual model over separate monolingual models?

A It eliminates the need for any language-specific preprocessing steps.

B It always achieves higher accuracy on every language than monolingual models.

C It requires less total training data because it shares knowledge across languages.

D It guarantees zero bias in all languages it supports.

💡 Hint

Think about how sharing information between languages can help when some languages have less data.

Multilingual models share parameters across languages, allowing them to learn from more data collectively. This helps especially low-resource languages. However, they do not always outperform monolingual models on every language, nor do they remove all preprocessing or bias.

❓ Predict Output

intermediate

Output of tokenization in a multilingual model

Given the following code snippet using a multilingual tokenizer, what is the output tokens list?

NLP

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('bert-base-multilingual-cased')
text = 'Hello, 你好, مرحبا'
tokens = tokenizer.tokenize(text)
print(tokens)

A ['Hello', ',', '你', '好', ',', 'م', '##رح', '##با']

B ['Hello', ',', '你好', ',', 'مرحبا']

C ['Hello', ',', '你', '##好', ',', 'م', '##رح', '##با']

D ['Hello', ',', '你', '好', ',', 'مرحبا']

💡 Hint

Remember that BERT uses WordPiece tokenization which may split words into subwords marked by '##'.

The multilingual BERT tokenizer splits Chinese characters individually and Arabic words into subwords with '##'. So '你' and '好' are separate tokens, and 'مرحبا' is split into 'م', '##رح', '##با'.

❓ Model Choice

advanced

Choosing a model for low-resource languages

You want to build a text classification model for a low-resource language with very little labeled data. Which model choice is best?

A Train a monolingual model from scratch only on the low-resource language data.

B Use a large multilingual pretrained model fine-tuned on the low-resource language data.

C Use a monolingual pretrained model from a high-resource language and translate data.

D Train a multilingual model from scratch on all available languages.

💡 Hint

Consider how pretrained knowledge from many languages can help when labeled data is scarce.

Large multilingual pretrained models have learned shared representations across languages and can transfer knowledge to low-resource languages during fine-tuning, improving performance. Training from scratch or translating data may be less effective.

❓ Metrics

advanced

Evaluating multilingual model performance

You evaluate a multilingual model on three languages and get these F1 scores: English 0.85, Spanish 0.80, Swahili 0.60. Which metric best summarizes overall performance fairly?

A Weighted-average F1 score (weighted by number of samples per language).

B Micro-average F1 score (global average considering all samples).

C Macro-average F1 score (average of all languages' F1 scores).

D Maximum F1 score among the three languages.

💡 Hint

Think about how to fairly combine scores when languages have different data sizes.

Weighted-average F1 accounts for the number of samples per language, giving a fair overall performance measure when data sizes differ. Macro-average treats all languages equally regardless of size, micro-average pools all samples ignoring language, and max ignores other languages.

🔧 Debug

expert

Debugging poor performance on a multilingual model

You fine-tuned a multilingual model on a dataset with English and French texts. The model performs well on English but poorly on French. Which is the most likely cause?

A The optimizer used is incompatible with multilingual models.

B The model architecture does not support French language tokens.

C The training data for French was accidentally excluded during fine-tuning.

D The tokenizer was not properly set to the multilingual tokenizer, causing French text to be tokenized incorrectly.

💡 Hint

Check if the tokenizer matches the model and supports all languages in your data.

If the tokenizer is not set to a multilingual tokenizer, French text may be split incorrectly, leading to poor model input and performance. Model architectures generally support all tokens from their tokenizer. Data exclusion or optimizer incompatibility are less common causes.