The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

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 …

We present a survey covering the state of the art in low-resource machine translation (MT)
research. There are currently around 7,000 languages spoken in the world and almost all …

We present a simple and effective approach to incorporating syntactic structure into neural
attention-based encoder-decoder models for machine translation. We rely on graph …

Semantic representations have long been argued as potentially useful for enforcing
meaning preservation and improving generalization performance of machine translation …

In this paper, we extend an attention-based neural machine translation (NMT) model by
allowing it to access an entire training set of parallel sentence pairs even after training. The …

Recommendations can greatly benefit from good representations of the user state at
recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) …

We present a simple method to incorporate syntactic information about the target language
in a neural machine translation system by translating into linearized, lexicalized constituency …

Neural encoder-decoder models of machine translation have achieved impressive results,
while learning linguistic knowledge of both the source and target languages in an implicit …

Transformer-based neural machine translation (NMT) has recently achieved state-ofthe-art
performance on many machine translation tasks. However, recent work (Raganato and …