@inproceedings{kumar-etal-2026-reliable,
title = "How Reliable are {AMR} Parser Outputs? Uncertainty Estimation for {AMR} Parsing",
author = "Kumar, Anuj and
Ozaki, Shintaro and
Prasad, Yamuna and
Hayashi, Katsuhiko and
Kamigaito, Hidetaka and
Watanabe, Taro",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1365/",
pages = "31144--31159",
ISBN = "979-8-89176-459-0",
abstract = "Recent advances have significantly improved the performance of Abstract Meaning Representation (AMR) parsers, yet determining when their outputs can be trusted remains largely unexplored. This work presents the first study of uncertainty estimation for AMR parsers in a black-box setting using top-$k$ beam-search candidates. Uncertainty is estimated from pairwise candidate similarities and evaluated using the Prediction Rejection Ratio. This work characterises the relationship between parsing performance and uncertainty, evaluates the reliability of beam candidate distributions, analyses the effect of evaluation metrics on uncertainty assessment, and identifies parsers that provide the strongest balance between performance and reliability. Experimental results show the value of uncertainty estimation as a complementary dimension for evaluating AMR parsers."
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%0 Conference Proceedings
%T How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing
%A Kumar, Anuj
%A Ozaki, Shintaro
%A Prasad, Yamuna
%A Hayashi, Katsuhiko
%A Kamigaito, Hidetaka
%A Watanabe, Taro
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kumar-etal-2026-reliable
%X Recent advances have significantly improved the performance of Abstract Meaning Representation (AMR) parsers, yet determining when their outputs can be trusted remains largely unexplored. This work presents the first study of uncertainty estimation for AMR parsers in a black-box setting using top-k beam-search candidates. Uncertainty is estimated from pairwise candidate similarities and evaluated using the Prediction Rejection Ratio. This work characterises the relationship between parsing performance and uncertainty, evaluates the reliability of beam candidate distributions, analyses the effect of evaluation metrics on uncertainty assessment, and identifies parsers that provide the strongest balance between performance and reliability. Experimental results show the value of uncertainty estimation as a complementary dimension for evaluating AMR parsers.
%U https://aclanthology.org/2026.emnlp-main.1365/
%P 31144-31159
Markdown (Informal)
[How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing](https://aclanthology.org/2026.emnlp-main.1365/) (Kumar et al., EMNLP 2026)
ACL