[
Table 4: Performance on CoLA and STS-B, under different data-augmentation methods.
['[EMPTY]', 'CoLA', 'STS-B']
As can be seen, in general, back-translation achieves better results than EDA, except that the median performance of back-translation on STS-B is 0.1% lower than that of EDA. The reason that back-translation works better is probably because back-translation performs augmentation at the sentence level globally: the entire sentence is translated back and forth, while EDA performs augmentation at the word/phrase level locally. Therefore, back-translation can better capture the global semantics of sentences while EDA captures local semantics. Another reason might be that the sentences augmented by back-translation are more different from the original ones. In contrast, the sentences augmented by EDA are close to the original ones since EDA performs local edits of the original sentences and keeps the majority of a sentence untouched. It is more difficult to predict whether two augmented sentences by back-translation are from the same original sentence than to predict those augmented by EDA. Solving a more challenging CSSL task usually leads to better representations.