Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations
Table 3: The influence of fine-tuning parser parameters in the SAWR system.
['Parser', 'MT03', 'MT04', 'MT05', 'MT06', 'Average']
As an interesting attempt, we can simultaneously fine tune the parameters of both the parser and the Seq2Seq NMT model during training. We can see that fine-tuning decreases the average BLEU score by 38.83−37.69=1.14 significantly. This may be because that fine-tuning disorders the representation ability of the parser and makes its function more overlapping with other network components. This further demonstrates that pretrained syntax-aware word representations are helpful for NMT.