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 …

Selection of text feature item is a basic and important matter for text mining and information
retrieval. Traditional methods of feature extraction require handcrafted features. To hand …

We demonstrate that scaling up language models greatly improves task-agnostic, few-shot
performance, sometimes even becoming competitive with prior state-of-the-art fine-tuning …

“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …

We present TriviaQA, a challenging reading comprehension dataset containing over 650K
question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by …

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 …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

Visual question answering is fundamentally compositional in nature---a question like" where
is the dog?" shares substructure with questions like" what color is the dog?" and" where is …

Most tasks in natural language processing can be cast into question answering (QA)
problems over language input. We introduce the dynamic memory network (DMN), a neural …

Neural network architectures with memory and attention mechanisms exhibit certain reason-
ing capabilities required for question answering. One such architecture, the dynamic …