Addressing Zero-Resource Domains Using Document-Level Context in Neural Machine Translation
Table 2: Results on zero-resource domain adaptation for PatTR and TED. Best results in bold. †- statistical significance with p < 0.01, ‡- p < 0.05.
['[EMPTY]', 'PatTR', 'TED']
In zero-resource domain adaptation experiments, we do not use any data from TED or PatTR, either as training or development data. Unless otherwise specified, we always compare the models against the sentence-level baseline. We see that our document-level models are robust across the two domains. These results confirm our assumption that access to document-level context provides for a domain signal. Of course, these models are oblivious to the actual characteristics of the domain since it was not seen during training, but presumably, they managed to match the zero-resource domain to a similar one. We assume that the reason for the larger improvements on TED in comparison to PatTR is that TED is a more similar domain to the domains seen during training. As a result, matching TED sentences to seen domains was easier for all models.