The past year has witnessed rapid advances in sequence-to-sequence (seq2seq) modeling
for Machine Translation (MT). The classic RNN-based approaches to MT were first out …

This article presents the most up-to-date, influential automated, semiautomated and human
metrics used to evaluate the quality of machine translation (MT) output and provides the …

Compute, data, and algorithmic advances are the three fundamental factors that drive
progress in modern Machine Learning (ML). In this paper we study trends in the most readily …

We explore six challenges for neural machine translation: domain mismatch, amount of
training data, rare words, long sentences, word alignment, and beam search. We show both …

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

Sequence-to-sequence models have shown strong performance across a broad range of
applications. However, their application to parsing and generating text usingAbstract …

We investigate the use of extended context in attention-based neural machine translation.
We base our experiments on translated movie subtitles and discuss the effect of increasing …

The dream of automatic language translation is now closer thanks to recent advances in the
techniques that underpin statistical machine translation. This class-tested textbook from an …

Harnessing the statistical power of neural networks to perform language understanding and
symbolic reasoning is difficult, when it requires executing efficient discrete operations …

In a partially observable Markov decision process (POMDP), an agent typically uses a
representation of the past to approximate the underlying MDP. We propose to utilize a frozen …