Simplify-then-Translate: Automatic Preprocessing for Black-Box Translation
Table 7: Simplification performance of APP models trained on corpora generated by En-Es and En-Ru datasets on Turk and PWKP open source datasets.
['(Metric) Dataset', 'Wiki', 'En-Es', 'En-Ru']
We also test the performance of an in-domain simplification model trained on the WikiLarge dataset. As expected, the performance of the in-domain model exceeds the performance of the models trained on En-Es and En-Ru datasets. It is also interesting to note that the En-Es model outperforms the En-Ru models on both datasets, underlining our hypothesis that easier language pairs result in better APP models. This idea can be used to inform the high-resource language pair(s) to pick for training an APP model for a target language pair. For instance, to simplify English it would be better to pick a high-resource pair which is easier to translate from English, e.g. Spanish. While simplifying Catalan(ca), it would be good to pick a translation pair like Catalan-Spanish which would be easier to translate than Catalan-English.