Constructive Type-Logical Supertagging with Self-Attention Networks
Table 2: Repetition-averaged unseen type generation and precision.
['Model', 'New Types', 'Unique', 'Correct (%)']
We are also interested in quantifying the models’ “imaginative” precision, i.e., how often do they generate new types to analyze a given input sentence, and, when they do, how often are they right Although all constructive models are eager to produce types never seen during training, they do so to a reasonable extent. Similar to their accuracy, an upwards trend is also seen in their precision, with M0 getting the largest percentage of generated types correct.