Fast Cross-domain Data Augmentation through Neural Sentence Editing
Table 2: Performance of the Edit-transformer vs the Neural-editor
['[EMPTY]', 'Edit-transformer Yelp test', 'Edit-transformer Yelp test', 'Edit-transformer Wikipedia test', 'Edit-transformer Wikipedia test', 'Neural-editor Yelp test', 'Neural-editor Yelp test', 'Neural-editor Wikipedia test', 'Neural-editor Wikipedia test']
we show the results from the comparison. As expected, both models performed significantly better on same-domain data, compared to out-of-domain. However, the edit-transformer results are better than the neural-editor, both on same-domain and cross-domain data. Testing both models on same-domain data the edit-transformer is on average 3 times better than the neural-editor. While on out-of-domain it is at least 1.3 times better than the neural-editor.