The ability to analyse online user-generated content related to sentiments (eg, thoughts and
opinions) on products or policies has become a de-facto skillset for many companies and …

Machine translation systems achieve near human-level performance on some languages,
yet their effectiveness strongly relies on the availability of large amounts of parallel …

We explore six challenges for neural machine translation: domain mismatch, amount of
training data, rare words, long sentences, word alignment, and beam search. We show both …

Deep learning is revolutionizing how machine translation systems are built today. This book
introduces the challenge of machine translation and evaluation-including historical …

Word embeddings are well known to capture linguistic regularities of the language on which
they are trained. Researchers also observe that these regularities can transfer across …

The dream of automatic language translation is now closer thanks to recent advances in the
techniques that underpin statistical machine translation. This class-tested textbook from an …

Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned
bilingual lexicons or on distribution matching, often with an assumption about the isometry of …

We observe that the development cross-entropy loss of supervised neural machine
translation models scales like a power law with the amount of training data and the number …

Supplying a comprehensive overview of healthcare analytics research, Healthcare Data
Analytics provides an understanding of the analytical techniques currently available to solve …

Despite being the seventh most widely spoken language in the world, Bengali has received
much less attention in machine translation literature due to being low in resources. Most …