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A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing. (arXiv:2010.12676v2 [cs.CL] UPDATED)
Oct. 26, 2022, 1:12 a.m. | Chunchuan Lyu, Shay B. Cohen, Ivan Titov
cs.LG updates on arXiv.org arxiv.org
Abstract Meaning Representations (AMR) are a broad-coverage semantic
formalism which represents sentence meaning as a directed acyclic graph. To
train most AMR parsers, one needs to segment the graph into subgraphs and align
each such subgraph to a word in a sentence; this is normally done at
preprocessing, relying on hand-crafted rules. In contrast, we treat both
alignment and segmentation as latent variables in our model and induce them as
part of end-to-end training.
As marginalizing over the structured latent …
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