@inproceedings{martinez-lorenzo-etal-2023-amrs,
title = "{AMR}s Assemble! Learning to Ensemble with Autoregressive Models for {AMR} Parsing",
author = "Mart{\'\i}nez Lorenzo, Abelardo Carlos and
Huguet Cabot, Pere Llu{\'\i}s and
Navigli, Roberto",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.137",
doi = "10.18653/v1/2023.acl-short.137",
pages = "1595--1605",
abstract = "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 [\url{https://www.github.com/babelscape/AMRs-Assemble}](\url{https://www.github.com/babelscape/AMRs-Assemble}).",
}
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%0 Conference Proceedings
%T AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing
%A Martínez Lorenzo, Abelardo Carlos
%A Huguet Cabot, Pere Lluís
%A Navigli, Roberto
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F martinez-lorenzo-etal-2023-amrs
%X 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 [https://www.github.com/babelscape/AMRs-Assemble](https://www.github.com/babelscape/AMRs-Assemble).
%R 10.18653/v1/2023.acl-short.137
%U https://aclanthology.org/2023.acl-short.137
%U https://doi.org/10.18653/v1/2023.acl-short.137
%P 1595-1605
Markdown (Informal)
[AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing](https://aclanthology.org/2023.acl-short.137) (Martínez Lorenzo et al., ACL 2023)
ACL