title: Phrase-Based Attentions
abstract: Most state-of-the-art neural machine translation systems, despite being different
  in architectural skeletons (e.g., recurrence, convolutional), share an indispensable
  feature: the Attention. However, most existing attention methods are token-based
  and ignore the importance of phrasal alignments, the key ingredient for the success
  of phrase-based statistical machine translation. In this paper, we propose
  novel phrase-based attention methods to model n-grams of tokens as attention
  entities. We incorporate our phrase-based attentions into the recently proposed
  Transformer network, and demonstrate that our approach yields improvements of
  1.3 BLEU for English-to-German and 0.5 BLEU for German-to-English translation
  tasks, and 1.75 and 1.35 BLEU points in English-to-Russian and Russian-to-English translation tasks 
  on WMT newstest2014 using WMT’16 training data.
  
authors: Phi Xuan Nguyen Shafiq Joty
hIndex: Shafiq Joty 39