Neural Shift-Reduce CCG Semantic Parsing

We present a shift-reduce CCG semantic parser. Our parser uses a neural network architecture that balances model capacity and computational cost. We train by transferring a model from a computationally expensive loglinear CKY parser. Our learner addresses two challenges: selecting the best parse for learning when the CKY parser generates multiple correct trees, and learning from partial derivations when the CKY parser fails to parse. We evaluate on AMR parsing. Our parser performs comparably to the CKY parser, while doing significantly fewer operations. We also present results for greedy semantic parsing with a relatively small drop in performance.