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 …

Many natural language processing (NLP) tasks appear in dual forms, which are generally
solved by dual learning technique that models the dualities between the coupled tasks. In …

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

The development of deep learning techniques has allowed Neural Machine Translation
(NMT) models to become extremely powerful, given sufficient training data and training time …

Training data for NLP tasks often exhibits gender bias in that fewer sentences refer to
women than to men. In Neural Machine Translation (NMT) gender bias has been shown to …

It is intuitive that semantic representations can be useful for machine translation, mainly
because they can help in enforcing meaning preservation and handling data sparsity (many …

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 …

Given a sentence (eg,“I like mangoes”) and a constraint (eg, sentiment flip), the goal of
controlled text generation is to produce a sentence that adapts the input sentence to meet …

There has been relatively little attention to incorporating linguistic prior to neural machine
translation. Much of the previous work was further constrained to considering linguistic prior …