Summary

This course will prepare students for real-world research in Natural Language Processing, considering ethical requirements from society as well as research communities. The course will explore how biases in data and models can be both identified (using statistical analysis, adversarial attacks, and model explanations) and mitigated (with modified training, fine-tuning, and dataset adaptation). Finally, this course will align students with best practices for research (including reproducible code and reporting model cards and data statements) as well as the challenges faced by research communities in languages other than English. By the end of the course, students will be able to think critically about ethical decisions in research and apply or extend common techniques to new datasets and models.

Action Required

• Presentation sign-up form
• Moderation sign-up form
• Capstone project proposals
• Check attendance / participation scores (read only)
  • Note: Virtual attendance has not yet been added

Time

Wed: 16:00~17:30 / Seoul Campus Building 9 Room 9509

Thu: 16:00~17:30 / Seoul Campus Building 9 Room 9509

Key Topics

• Issues in word embeddings and large-scale language models
• Biases in datasets
• Mitigating biases through modeling
• Privacy preservation issues
• Model explanations and interpretability
• Causal reasoning for NLP
• Low-resource and multi-lingual NLP
• Adversarial Attacks