jp-evalb: Robust Alignment-based PARSEVAL Measures

Jungyeul Park, Junrui Wang, Eunkyul Jo, Angela Park


Abstract
We introduce an evaluation system designed to compute PARSEVAL measures, offering a viable alternative to evalb commonly used for constituency parsing evaluation. The widely used evalb script has traditionally been employed for evaluating the accuracy of constituency parsing results, albeit with the requirement for consistent tokenization and sentence boundaries. In contrast, our approach, named jp-evalb, is founded on an alignment method. This method aligns sentences and words when discrepancies arise. It aims to overcome several known issues associated with evalb by utilizing the ‘jointly preprocessed (JP)’ alignment-based method. We introduce a more flexible and adaptive framework, ultimately contributing to a more accurate assessment of constituency parsing performance.
Anthology ID:
2024.naacl-demo.7
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kai-Wei Chang, Annie Lee, Nazneen Rajani
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
70–77
Language:
URL:
https://aclanthology.org/2024.naacl-demo.7
DOI:
Bibkey:
Cite (ACL):
Jungyeul Park, Junrui Wang, Eunkyul Jo, and Angela Park. 2024. jp-evalb: Robust Alignment-based PARSEVAL Measures. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations), pages 70–77, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
jp-evalb: Robust Alignment-based PARSEVAL Measures (Park et al., NAACL 2024)
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PDF:
https://aclanthology.org/2024.naacl-demo.7.pdf