AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR
  Parsing
Abelardo Carlos Martínez Lorenzo Pere-Lluís Huguet Cabot Roberto Navigli
In this paper, we examine the current state-of-the-art in AMR parsing, which
relies on ensemble strategies by merging multiple graph predictions. Our
analysis reveals that the present models often violate AMR structural
constraints. To address this issue, we develop a validation method, and show
how ensemble models can exploit SMATCH metric weaknesses to obtain higher
scores, but sometimes result in corrupted graphs. Additionally, we highlight
the demanding need to compute the SMATCH score among all possible predictions.
To overcome these challenges, we propose two novel ensemble strategies based on
Transformer models, improving robustness to structural constraints, while also
reducing the computational time. Our methods provide new insights for enhancing
AMR parsers and metrics. Our code is available at
\href{https://www.github.com/babelscape/AMRs-Assemble}{github.com/babelscape/AMRs-Assemble}.