A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing

• URL: http://arxiv.org/abs/2010.12676v1
• Date: Fri, 23 Oct 2020 21:22:50 GMT
• Title: A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing
• Authors: Chunchuan Lyu, Shay B. Cohen, Ivan Titov
• Abstract summary: We treat alignment and segmentation as latent variables in our model and induce them as part of end-to-end training. Our method also approaches that of a model that relies on citetLyu2018AMRPA's segmentation rules, which were hand-crafted to handle individual AMR constructions.
• Score: 75.36126971685034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 variables is infeasible, we use the variational autoencoding framework. To ensure end-to-end differentiable optimization, we introduce a continuous differentiable relaxation of the segmentation and alignment problems. We observe that inducing segmentation yields substantial gains over using a `greedy' segmentation heuristic. The performance of our method also approaches that of a model that relies on \citet{Lyu2018AMRPA}'s segmentation rules, which were hand-crafted to handle individual AMR constructions.

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