Refining Source Representations with Relation Networks for Neural Machine Translation
Table 2: Performance comparison on WMT17 En-De datasets.
['[BOLD] Systems', '[BOLD] test16']
Given the above results, we can conclude that RN can indeed learn the relationships between the source words and these relationships are useful and bring improvement on the translation performance. We also compared our method with the work of Bastings et al. \shortcitebastings2017graph which requires the injection of external syntactic knowledge, to see whether the relationships produced by RNs can lead to better translation than the syntax from supervised learning. We believe that the relationships of the source words derived from RNs do not necessarily conform to human cognition, but it can be simultaneously tailored with the other parts of the translation system. In this way, RNs can generate the relationships more suitable for the NMT.