Thus, the parameters of the gating GCN are trained from the relevance loss and the usual decoding loss, a ML objective over the gold sequence of decisions that output the query y. Discriminative re-ranking Global gating provides a more accurate model for softly predicting the correct subset of DB constants.
experimental setup We train and evaluate on SPIDeR (Yu et al., 2018b), which contains 7,000/1,034/2,147 train/development/test examples, using the same pre-processing as .