Over the past few years, neural networks have re-emerged as powerful machine-learning
models, yielding state-of-the-art results in fields such as image recognition and speech …

Deep learning approaches have achieved great success in the field of Natural Language
Processing (NLP). However, directly training deep neural models often suffer from overfitting …

Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are
simultaneously learned by a shared model. Such approaches offer advantages like …

Abstract We propose UniT, a Unified Transformer model to simultaneously learn the most
prominent tasks across different domains, ranging from object detection to natural language …

Transfer learning has recently become the dominant paradigm of machine learning. Pre-
trained models fine-tuned for downstream tasks achieve better performance with fewer …

Recent advances in language modeling using recurrent neural networks have made it
viable to model language as distributions over characters. By learning to predict the next …

Inductive transfer learning has greatly impacted computer vision, but existing approaches in
NLP still require task-specific modifications and training from scratch. We propose Universal …

Computational linguistics has enabled the introduction of objective tools that measure some
of the symptoms of schizophrenia, including the coherence of speech associated with formal …

Deep multitask networks, in which one neural network produces multiple predictive outputs,
can offer better speed and performance than their single-task counterparts but are …

Neural networks are a family of powerful machine learning models and this book focuses on
their application to natural language data. The first half of the book (Parts I and II) covers the …