AMR Similarity Metrics from Principles

Juri Opitz, Letitia Parcalabescu, Anette Frank


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; and iii) we propose a novel metric S2 match 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.
Anthology ID:
2020.tacl-1.34
Volume:
Transactions of the Association for Computational Linguistics, Volume 8
Month:
Year:
2020
Address:
Cambridge, MA
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
522–538
Language:
URL:
https://aclanthology.org/2020.tacl-1.34
DOI:
10.1162/tacl_a_00329
Bibkey:
Cite (ACL):
Juri Opitz, Letitia Parcalabescu, and Anette Frank. 2020. AMR Similarity Metrics from Principles. Transactions of the Association for Computational Linguistics, 8:522–538.
Cite (Informal):
AMR Similarity Metrics from Principles (Opitz et al., TACL 2020)
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PDF:
https://aclanthology.org/2020.tacl-1.34.pdf