Papers › Anchor and Broadcast: An Efficient Concept Alignment Approach for Evaluation of Semantic Graphs
Anchor and Broadcast: An Efficient Concept Alignment Approach for Evaluation of Semantic Graphs
Haibo Sun, Nianwen Xue
In this paper, we present AnCast, an intuitive and efficient tool for evaluating graph-based meaning representations (MR). AnCast implements evaluation metrics that are well understood in the NLP community, and they include concept F1, unlabeled relation F1, labeled relation F1, and weighted relation F1. The efficiency of the tool comes from a novel anchor broadcast alignment algorithm that is not subject to the trappings of local maxima. We show through experimental results that the AnCast score is highly correlated with the widely used Smatch score, but its computation takes only about 40% the time.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| AMR Graph Similarity | Benchmark for AMR Metrics based on Overt Objectives | ancast | Pearson’s ρ (amean) | 59.95 | #1 of 9 | Archive leaderboard | report |
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