SeqScore: Addressing Barriers to Reproducible Named Entity Recognition Evaluation
arXiv:2107.14154
Abstract
To address a looming crisis of unreproducible evaluation for named entity recognition, we propose guidelines and introduce SeqScore, a software package to improve reproducibility. The guidelines we propose are extremely simple and center around transparency regarding how chunks are encoded and scored. We demonstrate that despite the apparent simplicity of NER evaluation, unreported differences in the scoring procedure can result in changes to scores that are both of noticeable magnitude and statistically significant. We describe SeqScore, which addresses many of the issues that cause replication failures.
To appear in proceedings of Eval4NLP 2021 (EMNLP 2021 workshop)