We describe an open-source toolkit for neural machine translation (NMT). The toolkit
prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research …

We introduce a class of probabilistic continuous translation models called Recurrent
Continuous Translation Models that are purely based on continuous representations for …

This paper presents the results of the WMT14 shared tasks, which included a standard news
translation task, a separate medical translation task, a task for run-time estimation of …

The distributional hypothesis of Harris (1954), according to which the meaning of words is
evidenced by the contexts they occur in, has motivated several effective techniques for …

Continuous space word embeddings learned from large, unstructured corpora have been
shown to be effective at capturing semantic regularities in language. In this paper we …

This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a
multidimensional grid that can be applied to vectors, sequences or higher dimensional data …

We present KenLM, a library that implements two data structures for efficient language
model queries, reducing both time and memory costs. The PROBING data structure uses …

Neural Machine Translation (NMT) is a new approach to machine translation that has shown
promising results that are comparable to traditional approaches. A significant weakness in …

This paper introduces and summarises the findings of a new shared task at the intersection
of Natural Language Processing and Computer Vision: the generation of image descriptions …

We present a novel technique for learning semantic representations, which extends the
distributional hypothesis to multilingual data and joint-space embeddings. Our models …