  Recent advancements in attention mechanisms have replaced recurrent neural
networks and its variants for machine translation tasks. Transformer using
attention mechanism solely achieved state-of-the-art results in sequence
modeling. Neural machine translation based on the attention mechanism is
parallelizable and addresses the problem of handling long-range dependencies
among words in sentences more effectively than recurrent neural networks. One
of the key concepts in attention is to learn three matrices, query, key, and
value, where global dependencies among words are learned through linearly
projecting word embeddings through these matrices. Multiple query, key, value
matrices can be learned simultaneously focusing on a different subspace of the
embedded dimension, which is called multi-head in Transformer. We argue that
certain dependencies among words could be learned better through an
intermediate context than directly modeling word-word dependencies. This could
happen due to the nature of certain dependencies or lack of patterns that lend
them difficult to be modeled globally using multi-head self-attention. In this
work, we propose a new way of learning dependencies through a context in
multi-head using convolution. This new form of multi-head attention along with
the traditional form achieves better results than Transformer on the WMT 2014
English-to-German and English-to-French translation tasks. We also introduce a
framework to learn POS tagging and NER information during the training of
encoder which further improves results achieving a new state-of-the-art of 32.1
BLEU, better than existing best by 1.4 BLEU, on the WMT 2014 English-to-German
and 44.6 BLEU, better than existing best by 1.1 BLEU, on the WMT 2014
English-to-French translation tasks. We call this Transformer++.
