We introduce an evaluation system designed to compute PARSEVAL measures, offering a viable alternative to \texttt{evalb} commonly used for constituency parsing evaluation. The widely used \texttt{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 \texttt{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 \texttt{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.
@article{arxiv.2405.14150,
title = {jp-evalb: Robust Alignment-based PARSEVAL Measures},
author = {Jungyeul Park and Junrui Wang and Eunkyul Leah Jo and Angela Yoonseo Park},
journal= {arXiv preprint arXiv:2405.14150},
year = {2024}
}
Comments
To appear in The system demonstration track at NAACL-HLT 2024