  As Abstract Meaning Representation (AMR) implicitly involves compound
semantic annotations, we hypothesize auxiliary tasks which are semantically or
formally related can better enhance AMR parsing. We find that 1) Semantic role
labeling (SRL) and dependency parsing (DP), would bring more performance gain
than other tasks e.g. MT and summarization in the text-to-AMR transition even
with much less data. 2) To make a better fit for AMR, data from auxiliary tasks
should be properly "AMRized" to PseudoAMR before training. Knowledge from
shallow level parsing tasks can be better transferred to AMR Parsing with
structure transform. 3) Intermediate-task learning is a better paradigm to
introduce auxiliary tasks to AMR parsing, compared to multitask learning. From
an empirical perspective, we propose a principled method to involve auxiliary
tasks to boost AMR parsing. Extensive experiments show that our method achieves
new state-of-the-art performance on different benchmarks especially in
topology-related scores.
