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AI Research
arXiv CS.AI·3d ago·5 min read
How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing
TL;DR
This study introduces a method for estimating uncertainty in Abstract Meaning Representation (AMR) parser outputs, revealing the reliability of these outputs through analysis of beam-search candidates. It demonstrates that uncertainty estimation is a valuable metric for evaluating AMR parsers alongside traditional performance measures.
✦ Why It Matters
Incorporate uncertainty estimation in your AMR parser evaluations to enhance output reliability.
Key Takeaways
- Use top-k beam-search candidates for uncertainty estimation.
- Evaluate AMR parser outputs with uncertainty metrics.
- Analyze pairwise candidate similarities for reliability insights.
- Identify parsers that balance performance and reliability.
How It Works
Uncertainty is estimated by analyzing the similarities between top-k candidates generated by the AMR parser. The Prediction Rejection Ratio is then used to quantify this uncertainty, allowing for a more nuanced evaluation of parser outputs.