Addressing Zero-Resource Domains Using Document-Level Context in Neural Machine Translation
Table 9: Results from the ablation study. Each row shows which domain is used as the test set and each column shows from which domain the context originates.
['domain', 'Europarl', 'NewsComm', 'OpenSub', 'Rapid', 'Ubuntu', 'PatTR', 'TED', 'True']
On OpenSubtitles, Rapid, PatTR and TED, DomEmb(avg) improves on the sentence-level baseline if presented with context from the same domain (which is usually not from the same document). On Europarl, NewsCommentary and Ubuntu, it performs similarly to the baseline. In almost all cases, providing a mismatched context degrades the performance of the original DomEmb(avg). The results show that the model is relatively robust to incorrect, but closely related context which provides evidence for our hypothesis that DomEmb captures domain-relevant features. However, the correct context is important to obtain the best results across all domains.