NLP
hard
~35 hours
Multilingual NER with Zero-Shot Transfer
Build a named entity recognition system that trains on English data and transfers to 5+ languages without target-language training data. Implement entity-level evaluation to measure true generalization.
Skills Demonstrated
Cross-lingual NLP
Zero-shot transfer
Entity-level evaluation methodology
Tokenizer-aware data processing
Implementation Steps
- Fine-tune XLM-R on English CoNLL-2003 NER data
- Evaluate zero-shot transfer on 5 target languages
- Implement entity-level splits for honest evaluation
- Add entity replacement augmentation for robustness
- Build error analysis dashboard by entity type and language
- Compare with few-shot prompting approach using LLMs
Interview Relevance
Why this project matters for interviews
Multilingual NLP is critical for global products at Google, Meta, Microsoft, and Amazon. Zero-shot transfer shows understanding of representation learning beyond surface-level fine-tuning.