AMRize, then Parse! Enhancing AMR Parsing with PseudoAMR Data

Abstract

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. With carefully designed control experiments, we find that 1) Semantic role labeling (SRL) and dependency parsing (DP), would bring much more significant performance gain than unrelated tasks in the text-to-AMR transition. 2) To make a better fit for AMR, data from auxiliary tasks should be properly "AMRized'' to PseudoAMR before training. 3) Intermediate-task training paradigm outperforms multitask learning when introducing auxiliary tasks to AMR parsing. From an empirical perspective, we propose a principled method to choose, reform, and train auxiliary tasks to boost AMR parsing. Extensive experiments show that our method achieves new state-of-the-art performance on in-distribution, out-of-distribution, and few-shots benchmarks of AMR parsing.

Tasks

Reproductions