By contrast, in the CL learning regime, the incremental loss is computed as − log p (y t ), where p (y t ) is normalized only over the values of y t that are possible continuations once the grammar-induced constraints are taken into account, ignoring whatever weights the RNN predictor may (wrongly) believe should be put on impossible continuations.
the DSP-CL model incorporates the prior knowledge about well-formed derivation sequences that we have thanks to the grammar.