On the Similarities Between Native, Non-native and Translated Texts
Table 4: Perplexity: fitness of Germanic and Romance translationese LMs to Germanic and Romance NN test sets.
['LM / NN GerT', 'GerNN 8.77', 'LM / NN GerT', 'RomNN 8.64']
Prediction of GerNN by the GerT language model yields a slightly lower perplexity (i.e., a better prediction) than prediction by RomT. Similarly, RomNN is much better predicted by RomT than by GerT. These differences are statistically significant: we divided the NN texts into 50 chunks of 100 sentences each, and computed perplexity values by the two LMs for each chunk. Significance was then computed by a two-tailed paired t-test, yielding p-values of 0.015 for GerNN and 6e-22 for RomNN.