Statistical machine translation (SMT) treats the translation of natural language as a machine
learning problem. By examining many samples of human-produced translation, SMT …

Abstract Semantic Role Labeling (SRL) is believed to be a crucial step towards natural
language understanding and has been widely studied. Recent years, end-to-end SRL with …

Word embedding, where semantic and syntactic features are captured from unlabeled text
data, is a basic procedure in Natural Language Processing (NLP). The extracted features …

We propose a unified neural network architecture and learning algorithm that can be applied
to various natural language processing tasks including part-of-speech tagging, chunking …

Abstract Neural Machine Translation (NMT) based on the encoder-decoder architecture has
recently become a new paradigm. Researchers have proven that the target-side …

We describe a single convolutional neural network architecture that, given a sentence,
outputs a host of language processing predictions: part-of-speech tags, chunks, named …

While end-to-end neural machine translation (NMT) has made remarkable progress
recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel …

Neural Machine Translation (NMT) is a new approach for automatic translation of text from
one human language into another. The basic concept in NMT is to train a large Neural …

This paper studies the use of language models as a source of synthetic unlabeled text for
NLP. We formulate a general framework called “generate, annotate, and learn (GAL)” to take …

Despite the many proposals to solve the neural machine translation (NMT) problem of low-
resource languages, it continues to be difficult. The issue becomes even more complicated …