Unified Semantic Parsing with Weak Supervision
Table 2: Test denotation accuracy for each domain comparing our proposed method DistillCombined with the three baselines. We also report the skyline Supervised.
['DOMAIN', 'Weak- Independent', 'Weak- Combined', 'Distill- Independent', 'Distill- Combined', 'Supervised']
To reduce the variance in gradient estimation we use baseline b(x)=1|B|∑z∈BR(x,z) i.e. the average reward for the beam corresponding to the input instance x. See Note that the primary challenge with this weakly supervised training is the sparsity in reward signal given the large search space leading to only a few predictions having a non-zero reward. The average denotation accuracy (with beam width 1) of 70.6% which is comparable to state-of-the-art Jia and Liang This additionally suggests that data normalization process does not alter the task complexity.