Abstract
Abstract meaning representations (AMRs) are broad-coverage sentence-level semantic representations. AMRs represent sentences as rooted labeled directed acyclic graphs. AMR parsing is challenging partly due to the lack of annotated alignments between nodes in the graphs and words in the corresponding sentences. We introduce a neural parser which treats alignments as latent variables within a joint probabilistic model of concepts, relations and alignments. As exact inference requires marginalizing over alignments and is infeasible, we use the variational autoencoding framework and a continuous relaxation of the discrete alignments. We show that joint modeling is preferable to using a pipeline of align and parse. The parser achieves the best reported results on the standard benchmark (74.4% on LDC2016E25).
Highlights
Abstract meaning representations (AMRs) (Banarescu et al, 2013) are broad-coverage sentencelevel semantic representations
As AMR abstracts away from details of surface realization, it is potentially beneficial in many semantic related NLP tasks, including text summarization (Liu et al, 2015; Dohare and Karnick, 2017), machine translation (Jones et al, 2012) and question answering (Mitra and Baral, 2016)
AMR banks are a lot smaller than parallel corpora used in machine translation (MT) and it is important to inject a useful inductive bias
Summary
Abstract meaning representations (AMRs) (Banarescu et al, 2013) are broad-coverage sentencelevel semantic representations. As AMR abstracts away from details of surface realization, it is potentially beneficial in many semantic related NLP tasks, including text summarization (Liu et al, 2015; Dohare and Karnick, 2017), machine translation (Jones et al, 2012) and question answering (Mitra and Baral, 2016). One distinctive aspect of AMR annotation is the lack of explicit alignments between nodes in the graph (concepts) and words in the sentences. Though this arguably simplified the annotation process (Banarescu et al, 2013), it is not straightforward to produce an effective parser without relying on an alignment. The aligners are not directly informed by the AMR parsing objective and may produce alignments suboptimal for this task
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