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 …

In recent years, natural language processing (NLP) has got great development with deep
learning techniques. In the sub-field of machine translation, a new approach named Neural …

Recent work in visual representation learning for robotics demonstrates the viability of
learning from large video datasets of humans performing everyday tasks. Leveraging …

Neural networks are a family of powerful machine learning models. This book focuses on the
application of neural network models to natural language data. The first half of the book …

The encoder-decoder framework for neural machine translation (NMT) has been shown
effective in large data scenarios, but is much less effective for low-resource languages. We …

Deep learning is revolutionizing how machine translation systems are built today. This book
introduces the challenge of machine translation and evaluation-including historical …

In this paper, we propose a novel neural network model called RNN Encoder-Decoder that
consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …

Language models are generally trained on data spanning a wide range of topics (eg, news,
reviews, fiction), but they might be applied to an a priori unknown target distribution (eg …

Recent work has shown success in using neural network language models (NNLMs) as
features in MT systems. Here, we present a novel formulation for a neural network joint …