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New method estimates reliability of Abstract Meaning Representation parsers

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for estimating the reliability of Abstract Meaning Representation (AMR) parsers. This approach, which operates in a black-box setting, analyzes pairwise similarities among candidate outputs from top-k beam-search. The study evaluates this uncertainty estimation using the Prediction Rejection Ratio and characterizes its relationship with parsing performance. The findings highlight the utility of uncertainty estimation as a valuable metric for assessing AMR parsers, identifying those that offer a strong balance between performance and trustworthiness. AI

IMPACT Improves trustworthiness of natural language understanding systems by providing a measure of confidence in their output.

RANK_REASON The cluster contains a research paper detailing a new method for uncertainty estimation in AMR parsers. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method estimates reliability of Abstract Meaning Representation parsers

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The cluster contains a research paper detailing a new method for uncertainty estimation in AMR parsers. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Anuj Kumar, Shintaro Ozaki, Yamuna Prasad, Katsuhiko Hayashi, Hidetaka Kamigaito, Taro Watanabe ·

    How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing

    arXiv:2610.13236v1 Announce Type: cross 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 unce…

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