Keeping Notes: Conditional Natural Language Generationwith a Scratchpad Mechanism
Table 1: Performance for non-scratchpad models are taken from He2018LayerWiseCB except Stanford NMT Luong and Manning (2015). ∗: model is 2 layers.
['Model', 'IWSLT14 De→En', 'IWSLT14 Es →En', 'IWSLT15 En→Vi']
We evaluate on the IWLST 14 English to German and Spanish to English translation datasets Cettolo et al. as well as the IWSLT 15 Cettolo et al. English to Vietnamese translation dataset. For IWSLT14 Cettolo et al. , we compare to the models evaluated by \newciteHe2018LayerWiseCB, which includes a transformer Vaswani et al. For IWSLT15, we primarily compare to GNMT Wu et al. Our approach achieves state-of-the-art or comparable results on all datasets.