jp-evalb: Robust Alignment-based PARSEVAL Measures
Jungyeul Park, Junrui Wang, Eunkyul Leah Jo, Angela Yoonseo 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.
BibTeX
@inproceedings{park-etal-2024-jp,
title = "jp-evalb: Robust Alignment-based {PARSEVAL} Measures",
author = "Park, Jungyeul and
Wang, Junrui and
Jo, Eunkyul Leah and
Park, Angela Yoonseo",
editor = "Chang, Kai-Wei and
Lee, Annie and
Rajani, Nazneen",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-demo.7/",
doi = "10.18653/v1/2024.naacl-demo.7",
pages = "70--77"
}