Improving Grammar-based Sequence-to-Sequence Modeling with Decomposition
  and Constraints
Chao Lou Kewei Tu
Neural QCFG is a grammar-based sequence-tosequence (seq2seq) model with
strong inductive biases on hierarchical structures. It excels in
interpretability and generalization but suffers from expensive inference. In
this paper, we study two low-rank variants of Neural QCFG for faster inference
with different trade-offs between efficiency and expressiveness. Furthermore,
utilizing the symbolic interface provided by the grammar, we introduce two soft
constraints over tree hierarchy and source coverage. We experiment with various
datasets and find that our models outperform vanilla Neural QCFG in most
settings.