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Intro

Remember, Neural Nets are Feature Extractors!

Reminder: Types of Learning

Standard Multi-task Learning

Selective Parameter Adaptation • Sometimes it is better to adapt only some of the parameters

Different Layers for Different Tasks (Hashimoto et al. 2017)

Multiple Annotation Standards

Supervised/Unsupervised Adaptation

Supervised Domain Adaptation through Feature Augmentation

Unsupervised Learning through Feature Matching

Multi-lingual Sequence-to- sequence Models

Multi-lingual Pre-training

Difficulties in Fully Multi- lingual Learning

Data Balancing

Cross-lingual Transfer Learning

What if languages don't share the same script?

Zero-shot Transfer to New Languages

Data Creation, Active Learning . In order to get in-language training data, Active Learning (AL) can be used

Description:

Explore multitask and multilingual learning in natural language processing through this comprehensive lecture from CMU's Neural Networks for NLP course. Delve into the fundamentals of multi-task learning, examining various methods and objectives specific to NLP. Investigate multilingual learning techniques, including multi-lingual sequence-to-sequence models and pre-training approaches. Learn about challenges in fully multi-lingual learning, such as data balancing and cross-lingual transfer. Discover strategies for handling languages with different scripts and implementing zero-shot transfer to new languages. Gain insights into data creation and active learning for obtaining in-language training data.

CMU Neural Nets for NLP 2020 - Multitask and Multilingual Learning

Graham Neubig

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#Computer Science #Artificial Intelligence #Neural Networks #Natural Language Processing (NLP) #Machine Learning #Transfer Learning #Active Learning #Multi-Task Learning #Sequence to Sequence Models

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