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 …

Neural machine translation (NMT) models typically operate with a fixed vocabulary, but
translation is an open-vocabulary problem. Previous work addresses the translation of out-of …

Neural machine translation (MT) models obtain state-of-the-art performance while
maintaining a simple, end-to-end architecture. However, little is known about what these …

What are the units of text that we want to model? From bytes to multi-word expressions, text
can be analyzed and generated at many granularities. Until recently, most natural language …

Neural Machine Translation (NMT) is a new approach for automatic translation of text from
one human language into another. The basic concept in NMT is to train a large Neural …

Previous work has shown that Chinese word segmentation is useful for machine translation
to English, yet the way different segmentation strategies affect MT is still poorly understood …

Neural machine translation (NMT) systems have recently achieved results comparable to the
state of the art on a few translation tasks, including English→ French and English→ German …

Parallel corpora are crucial for training SMT systems. However, for many language pairs
they are available only in very limited quantities. For these language pairs a huge portion of …

While a lot of work has been done in understanding representations learned within deep
NLP models and what knowledge they capture, work done towards analyzing individual …

Modelling language change is an increasingly important area of interest within the fields of
sociolinguistics and historical linguistics. In recent years, there has been a growing number …