# RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy

arXiv: https://arxiv.org/abs/1604.01278 · HTML: https://mlsanity.com/paper/1604.01278/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2016.

## Built on by (most cited first, 11 of 11)

- [It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk](https://mlsanity.com/paper/2310.01387/) (2023)
- [Maximum Bayes Smatch Ensemble Distillation for AMR Parsing](https://mlsanity.com/paper/2112.07790/) (2021)
- [Ensembling Graph Predictions for AMR Parsing](https://mlsanity.com/paper/2110.09131/) (2021)
- [Multilingual AMR Parsing with Noisy Knowledge Distillation](https://mlsanity.com/paper/2109.15196/) (2021)
- [Neural Semantic Parsing with Extremely Rich Symbolic Meaning Representations](https://mlsanity.com/paper/2404.12698/) (2024)
- [AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing](https://mlsanity.com/paper/2306.10786/) (2023)
- [Levi Graph AMR Parser using Heterogeneous Attention](https://mlsanity.com/paper/2107.04152/) (2021)
- [Cross-domain Generalization for AMR Parsing](https://mlsanity.com/paper/2210.12445/) (2022)
- [Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification](https://mlsanity.com/paper/2301.11459/) (2023)
- [Retrieval-Augmented Semantic Parsing: Improving Generalization with Lexical Knowledge](https://mlsanity.com/paper/2412.10207/) (2024)
- [Multi-Modal Semantic Parsing for the Interpretation of Tombstone Inscriptions](https://mlsanity.com/paper/2507.04377/) (2025)

## Abstract (arXiv)

Two extensions to the AMR smatch scoring script are presented. The first extension com-bines the smatch scoring script with the C6.0 rule-based classifier to produce a human-readable report on the error patterns frequency observed in the scored AMR graphs. This first extension results in 4% gain over the state-of-art CAMR baseline parser by adding to it a manually crafted wrapper fixing the identified CAMR parser errors. The second extension combines a per-sentence smatch with an en-semble method for selecting the best AMR graph among the set of AMR graphs for the same sentence. This second modification au-tomatically yields further 0.4% gain when ap-plied to outputs of two nondeterministic AMR parsers: a CAMR+wrapper parser and a novel character-level neural translation AMR parser. For AMR parsing task the character-level neural translation attains surprising 7% gain over the carefully optimized word-level neural translation. Overall, we achieve smatch F1=62% on the SemEval-2016 official scor-ing set and F1=67% on the LDC2015E86 test set.