We propose a refined alignment-based method to assess end-to-end grammatical error correction (GEC) systems, aiming to reproduce and improve results from existing evaluation tools, such as errant, even when applied to raw text input—reflecting real-world language learners’ writing scenarios. Our approach addresses challenges arising from sentence boundary detection deviations in text preprocessing, a factor overlooked by current GEC evaluation metrics. We demonstrate its effectiveness by replicating results through a re-implementation of errant, utilizing stanza for error annotation and simulating end-to-end evaluation from raw text. Additionally, we propose a potential multilingual errant, presenting Chinese and Korean GEC results. Previously, Chinese and Korean errant were implemented independently for each language, with different annotation formats. Our approach generates consistent error annotations across languages, establishing a basis for standardized grammatical error annotation and evaluation in multilingual GEC contexts.

A preprocessing task such as tokenization and sentence boundary detection (SBD) has commonly been considered as NLP challenges that have already been solved. This perception is due to their generally good performance and the presence of pre-tokenized data. However, it’s important to note that the low error rates of current methods are mainly specific to certain tasks, and rule-based tokenization can be difficult to use across different systems. Despite being subtle, these limitations are significant in the context of the NLP pipeline. In this paper, we introduce a novel evaluation algorithm for the preprocessing task, including both tokenization and SBD results. This algorithm aims to enhance the reliability of evaluations by reevaluating the counts of true positive cases for F1 measures in both preprocessing tasks jointly. It achieves this through an alignment-based approach inspired by sentence and word alignments used in machine translation. Our evaluation algorithm not only allows for precise counting of true positive tokens and sentence boundaries but also combines these two evaluation tasks into a single organized pipeline. To illustrate and clarify the intricacies of this calculation and integration, we provide detailed pseudo-code configurations for implementation. Additionally, we offer empirical evidence demonstrating how sentence and word alignment can improve evaluation reliability and present case studies to further support our approach.

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.