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Vol. 8 (2020)

TACL approved

AMR Similarity Metrics from Principles

Published 2020-09-10

  • Juri Opitz
    • ,
  • Letitia Parcalabescu
    • ,
  • Anette Frank

Juri Opitz
Heidelberg University

Letitia Parcalabescu
Heidelberg University

Anette Frank
Heidelberg University

Abstract

Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs. The canonical SMATCH metric (Cai and Knight, 2013) aligns the variables of two graphs and assesses triple matches. The recent SEMBLEU metric (Song and Gildea, 2019) is based on the machine-translation metric BLEU (Papineni et al., 2002) and increases computational efficiency by ablating the variable-alignment.

In this paper, i) we establish criteria that enable researchers to perform a principled assessment of metrics comparing meaning representations like AMR; ii) we undertake a thorough analysis of SMATCH and SEMBLEU where we show that the latter exhibits some undesirable properties.  For example, it does not conform to the identity of indiscernibles rule and introduces biases that are hard to control; iii) we propose a novel metric S2MATCH that is more benevolent to only very slight meaning deviations and targets the fulfilment of all established criteria. We assess its suitability and show its advantages over SMATCH and SEMBLEU.


 

Article at MIT Press Presented at NAACL 2021