Abstract
Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs. The major evaluation metric, SMATCH (Cai and Knight, 2013), searches for one-to-one mappings between the nodes of two AMRs with a greedy hill-climbing algorithm, which leads to search errors. We propose SEMBLEU, a robust metric that extends BLEU (Papineni et al., 2002) to AMRs. It does not suffer from search errors and considers non-local correspondences in addition to local ones. SEMBLEU is fully content-driven and punishes situations where a system’s output does not preserve most information from the input. Preliminary experiments on both sentence and corpus levels show that SEMBLEU has slightly higher consistency with human judgments than SMATCH. Our code is available at http://github.com/freesunshine0316/sembleu.- Anthology ID:
- P19-1446
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4547–4552
- Language:
- URL:
- https://aclanthology.org/P19-1446
- DOI:
- 10.18653/v1/P19-1446
- Cite (ACL):
- Linfeng Song and Daniel Gildea. 2019. SemBleu: A Robust Metric for AMR Parsing Evaluation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4547–4552, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- SemBleu: A Robust Metric for AMR Parsing Evaluation (Song & Gildea, ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P19-1446.pdf
- Code
- freesunshine0316/sembleu + additional community code