Controlling Personality-Based Stylistic Variation with Neural Natural Language Generators
Table 8: Correlations between Personage and models for aggregation operations in Table 3
['[BOLD] Model', '[BOLD] AGREE', '[BOLD] CONSC', '[BOLD] DISAG', '[BOLD] EXTRA', '[BOLD] UNCON']
Here, Model_NoSup actually frequently outperforms the more informed Model_Token. Note that all personalities use aggregation, even thought not all personalities use pragmatic markers, and so even without a special personality token, Model_NoSup is able to faithfully reproduce aggregation operations. In fact, since the correlations are frequently higher than those for Model_Token, we hypothesize that is able to more accurately focus on aggregation (common to all personalities) than stylistic differences, which Model_Token is able to produce.