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ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs. (arXiv:2204.08875v1 [cs.CL])
April 20, 2022, 1:11 a.m. | Liang Chen, Peiyi Wang, Runxin Xu, Tianyu Liu, Zhifang Sui, Baobao Chang
cs.CL updates on arXiv.org arxiv.org
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 …
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