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 …

Despite recent advances in natural language processing, many statistical models for
processing text perform extremely poorly under domain shift. Processing biomedical and …

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 …

This paper presents the results of the WMT17 shared tasks, which included three machine
translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and …

Unsupervised representation learning algorithms such as word2vec and ELMo improve the
accuracy of many supervised NLP models, mainly because they can take advantage of large …

Intent detection and slot filling are two main tasks for building a spoken language
understanding (SLU) system. The two tasks are closely tied and the slots often highly …

Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no
explicit linguistic features. However, prior work has shown that gold syntax trees can …

Transfer and multi-task learning have traditionally focused on either a single source-target
pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and …

Syntactic and semantic parsing has been investigated for decades, which is one primary
topic in the natural language processing community. This article aims for a brief survey on …

In this paper, we present a two-stage neural quality estimation model that uses multilevel
task learning for translation quality estimation (QE) at the sentence, word, and phrase levels …