Grammatical error correction (GEC) is an important application aspect of natural language
processing techniques, and GEC system is a kind of very important intelligent system that …

This paper describes the creation process and statistics of the official United Nations Parallel
Corpus, the first parallel corpus composed from United Nations documents published by the …

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 …

Grammar error correction (GEC) is an important application aspect of natural language
processing techniques. The past decade has witnessed significant progress achieved in …

In this paper we provide the largest published comparison of translation quality for phrase-
based SMT and neural machine translation across 30 translation directions. For ten …

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 …

Position encoding (PE), an essential part of self-attention networks (SANs), is used to
preserve the word order information for natural language processing tasks, generating fixed …

We address the problem of simultaneous translation by modifying the Neural MT decoder to
operate with dynamically built encoder and attention. We propose a tunable agent which …

In this work, we study parameter tuning towards the M^ 2 metric, the standard metric for
automatic grammar error correction (GEC) tasks. After implementing M^ 2 as a scorer in the …

We investigate three methods for integrating an unsupervised transliteration model into an
end-to-end SMT system. We induce a transliteration model from parallel data and use it to …