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

In this paper, we present Farasa, a fast and accurate Arabic segmenter. Our approach is
based on SVM-rank using linear kernels. We measure the performance of the segmenter in …

We examine how various types of noise in the parallel training data impact the quality of
neural machine translation systems. We create five types of artificial noise and analyze how …

Neural machine translation represents an exciting leap forward in translation quality. But
what longstanding weaknesses does it resolve, and which remain? We address these …

We contribute 5-gram counts and language models trained on the Common Crawl corpus, a
collection over 9 billion web pages. This release improves upon the Google n-gram counts …

We present cdec, an open source framework for decoding, aligning with, and training a
number of statistical machine translation models, including word-based models, phrase …

Conventional n-best reranking techniques often suffer from the limited scope of the nbest list,
which rules out many potentially good alternatives. We instead propose forest reranking, a …

The recently proposed neural network joint model (NNJM)(Devlin et al., 2014) augments the
n-gram target language model with a heuristically chosen source context window, achieving …

Translation rule extraction is a fundamental problem in machine translation, especially for
linguistically syntax-based systems that need parse trees from either or both sides of the …

Abstract Concept-to-text generation refers to the task of automatically producing textual
output from non-linguistic input. We present a joint model that captures content selection (" …