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.

采用自动标注工具 ERRANT，使用日本大学生写作样本对最先进的序列标记语法错误检测和纠正模型（SeqTagger）进行了性能评估。结果表明该模型在错误检测方面显示出高精度但也相对保守，主题分析发现冠词和介词是主要错误类型。

评估语法错误修正的有效性：基于日本背景的人工评估方法

Assessing the Efficacy of Grammar Error Correction: A Human Evaluation  Approach in the Japanese Context

Exposure errors in an image cause a degradation in the contrast and low
visibility in the content. In this paper, we address this problem and propose
an end-to-end exposure correction model in order to handle both under- and
overexposure errors with a single model. Our model contains an image encoder,
consecutive residual blocks, and image decoder to synthesize the corrected
image. We utilize perceptual loss, feature matching loss, and multi-scale
discriminator to increase the quality of the generated image as well as to make
the training more stable. The experimental results indicate the effectiveness
of proposed model. We achieve the state-of-the-art result on a large-scale
exposure dataset. Besides, we investigate the effect of exposure setting of the
image on the portrait matting task. We find that under- and overexposed images
cause severe degradation in the performance of the portrait matting models. We
show that after applying exposure correction with the proposed model, the
portrait matting quality increases significantly.
this https URL

提出了一种端到端的曝光校正模型，采用感知损失，特征匹配损失和多尺度辨别器来提高生成图像的质量并使训练更加稳定，实验结果表明其有效性。在大规模曝光数据集上，取得了最新的最优结果，并研究了图像曝光设置对画像抠图任务的影响，发现曝光过度和曝光不足的图像导致画像抠图质量严重降低，采用所提出的模型进行曝光校正后，画像抠图质量显著提高。