arXiv：cs.CL· Anuj Kumar, Shintaro Ozaki, Yamuna Prasad, Katsuhiko Hayashi, Hidetaka Kamigaito, Taro Watanabe·· 11 小时前
AMR 解析器输出有多可靠？AMR 解析的不确定性估计研究
How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing
AI 导读
研究首次在黑盒设定下用 top-k 波束搜索候选对 AMR 解析器做不确定性估计，通过候选两两相似度计算不确定性，并用 Prediction Rejection Ratio 评估。工作刻画了解析性能与不确定性的关系，评估波束候选分布的可靠性，并分析评测指标对不确定性评估的影响。
正文
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.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.13236 [cs.CL] |
| (or arXiv:2610.13236v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.13236 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Anuj Kumar [view email]
[v1]
Fri, 9 Oct 2026 08:53:02 UTC (217 KB)
来源：arXiv：cs.CL · arxiv.org