Addressing the Data Sparsity Issue in Neural AMR Parsing (Peng et al., EACL 2017)

Another paper looking at the issue of output symbol sparsity in AMR parsing, though here the solution is to group the consistent but rare symbols (rather than graph fragments like the paper last week). This drastically increases neural model performance, but does not reach the level of hybrid systems.

Getting the Most out of AMR Parsing (Wang and Xue, EMNLP 2017)

Two ideas for improving AMR parsing: (1) take graph distance into consideration when generating alignments, (2) during parsing, for concept generation, generate individual concepts in some cases and frequently occurring subgraphs in other cases.