Abstract

Abstract Meaning Representation (AMR) annotations are often assumed to closely mirror dependency syntax, but AMR explicitly does not require this, and the assumption has never been tested. To test it, we devise an expressive framework to align AMR graphs to dependency graphs, which we use to annotate 200 AMRs. Our annotation explains how 97% of AMR edges are evoked by words or syntax. Previously existing AMR alignment frameworks did not allow for mapping AMR onto syntax, and as a consequence they explained at most 23%. While we find that there are indeed many cases where AMR annotations closely mirror syntax, there are also pervasive differences. We use our annotations to test a baseline AMR-to-syntax aligner, finding that this task is more difficult than AMR-to-string alignment; and to pinpoint errors in an AMR parser. We make our data and code freely available for further research on AMR parsing and generation, and the relationship of AMR to syntax.

Highlights

  • Meaning Representation (AMR; Banarescu et al, 2013) is a popular framework for annotating whole sentence meaning

  • Abstract Meaning Representation (AMR) annotations include no explicit mapping between elements of an AMR and the corresponding elements of the sentence that evoke them, and this presents a challenge to developers of machine learning systems that parse sentences to AMR or generate sentences from AMR, since they must

  • Edge alignments allow ISI to explain more of the AMR structure than JAMR, but in a limited way: only 23% of AMR edges are aligned in the ISI corpus

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Summary

Introduction

Abstract Meaning Representation (AMR; Banarescu et al, 2013) is a popular framework for annotating whole sentence meaning. Flanigan et al, 2014; Wang et al, 2015; Artzi et al, 2015; Flanigan et al, 2016; Pourdamghani et al, 2016; Misra and Artzi, 2016; Damonte et al, 2017; Peng et al, 2017, inter alia).2 This AMR alignment problem was first formalized by Flanigan et al (2014), who mapped AMR nodes or connected subgraphs to words or sequences of words under the assumption of a oneto-one mapping—we call this JAMR alignment. Pourdamghani et al (2014) re-formalized it so that any AMR node or edge can map to any word without a one-to-one assumption—we call this ISI alignment.

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