Neural Phrase-to-Phrase Machine Translation

In recent years, we have witnessed the surge of neural sequence to sequence (seq2seq) models (Bah-danau et al., 2014; Sutskever et al., 2014). Gehring et al., 2017) and training techniques (V aswani et al., 2017; Ba et al., 2016) keep advancing Until recently, Huang et al. (2018) developed Neural Phrase-based Machine Translation This work was done when Jiangtao and Jiayuan interned in Google. We use "··· " to indicate all the possible segmentsx In our model, given the phrase-level attentions, we develop a dictionary lookup decoding method with an external phrase-to-phrase dictionary. We show how it avoids the more costly dynamic programming used in NPMT (Huang et al., For segment indexn 1,..., (a) Update the attention state given all previous segments, a Similar to NPMT in Huang et al. (2018), direct computing Eq. (5) is intractable. We also need to develop a dynamic programming algorithms to efficiently compute the loss function.