The end-to-end nature of neural machine translation (NMT) removes many ways of manually
guiding the translation process that were available in older paradigms. Recent work …

One of the difficulties of neural machine translation (NMT) is the recall and appropriate
translation of low-frequency words or phrases. In this paper, we propose a simple, fast, and …

Leveraging user-provided translation to constrain NMT has practical significance. Existing
methods can be classified into two main categories, namely the use of placeholder tags for …

We introduce an Edi t-Based T ransf O rmer with R epositioning (EDITOR), which makes
sequence generation flexible by seamlessly allowing users to specify preferences in output …

Abstract Neural Machine Translation (NMT) is a new approach to machine translation that
has made great progress in recent years. However, recent studies show that NMT generally …

In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our method
explicitly models the phrase structures in output sequences using Sleep-WAke Networks …

Due to the benefits of model compactness, multilingual translation (including many-to-one,
many-to-many and one-to-many) based on a universal encoder-decoder architecture …

Without real bilingual corpus available, unsupervised Neural Machine Translation (NMT)
typically requires pseudo parallel data generated with the back-translation method for the …

Conventional attention-based Neural Machine Translation (NMT) conducts dynamic
alignment in generating the target sentence. By repeatedly reading the representation of …

Phrases play an important role in natural language understanding and machine translation
(Sag et al., 2002; Villavicencio et al., 2005). However, it is difficult to integrate them into …