In this study, we evaluated the performance of the state-of-the-art sequence tagging grammar error detection and correction model (SeqTagger) using Japanese university students' writing samples. With an automatic annotation toolkit, ERRANT, we first evaluated SeqTagger's performance on error correction with human expert correction as the benchmark. Then a human-annotated approach was adopted to evaluate Seqtagger's performance in error detection using a subset of the writing dataset. Results indicated a precision of 63.66% and a recall of 20.19% for error correction in the full dataset. For the subset, after manual exclusion of irrelevant errors such as semantic and mechanical ones, the model shows an adjusted precision of 97.98% and an adjusted recall of 42.98% for error detection, indicating the model's high accuracy but also its conservativeness. Thematic analysis on errors undetected by the model revealed that determiners and articles, especially the latter, were predominant. Specifically, in terms of context-independent errors, the model occasionally overlooked basic ones and faced challenges with overly erroneous or complex structures. Meanwhile, context-dependent errors, notably those related to tense and noun number, as well as those possibly influenced by the students' first language (L1), remained particularly challenging.

翻译：本研究以日本大学生写作样本为语料，评估了最先进的序列标注语法错误检测与纠正模型（SeqTagger）的性能。首先以人工专家修正结果为基准，采用自动标注工具ERRANT评估SeqTagger在错误纠正方面的表现；随后通过人工标注方法，基于写作数据子集评估模型的错误检测能力。结果显示：在全量数据集中，模型错误纠正的精确率为63.66%，召回率为20.19%；在子集中，经手动剔除语义错误、机械性错误等无关错误后，模型错误检测的调整精确率达97.98%，调整召回率为42.98%，表明模型虽具高准确性但存在保守性。对模型未检出错误的主题分析显示，限定词与冠词（尤以冠词为主）占比最高。具体而言，在语境无关错误中，模型偶有遗漏基础性错误，且对错误密集或结构复杂的语句处理困难；而语境依赖型错误（尤涉及时态、名词数范畴及可能受学习者母语影响的错误）仍构成显著挑战。