Despite the recent success of deep neural networks in natural language processing and
other spheres of artificial intelligence, their interpretability remains a challenge. We analyze …

This article presents a review of the evolution of automatic post-editing, a term that describes
methods to improve the output of machine translation systems, based on knowledge …

This paper presents the results of the WMT16 shared tasks, which included five machine
translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three …

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 …

Neural machine translation (NMT) models learn representations containing substantial
linguistic information. However, it is not clear if such information is fully distributed or if some …

Recognizing and generating paraphrases is an important component in many natural
language processing applications. A well-established technique for automatically extracting …

Given the rise of the new neural approach to machine translation (NMT) and its promising
performance on different text types, we assess the translation quality it can attain on what is …

We aim to shed light on the strengths and weaknesses of the newly introduced neural
machine translation paradigm. To that end, we conduct a multifaceted evaluation in which …

In the context of recent improvements in the quality of machine translation (MT) output and
new use cases being found for that output, this article reports on an experiment using …

We conduct the first experiment in the literature in which a novel is translated automatically
and then post-edited by professional literary translators. Our case study is Warbreaker, a …