A Logic-Driven Framework for Consistency of Neural Models
Table 9: Choice of λ‘s for different consistency and corresponding unlabeled datasets. For different sizes of annotation and different types of data, we adopt different λ‘s.
['Data', '1%', '5%', '20%', '100%']
For different types of data and different consistency constraints, we used different weighting factors λ‘s. In general, we found that the smaller amount of labeled examples, the smaller λ for the symmetry and transitivity consistency. In contrast, the λ for M dataset can be much higher. We found a good value for M is 1. This is because the size of dataset U and T are fixed to be 100k, while the size of dataset M is the same as the amount of labeled examples.