Manually designing cloze test consumes enormous time and efforts. The major
challenge lies in wrong option (distractor) selection. Having carefully-design
distractors improves the effectiveness of learner ability assessment. As a
result, the idea of automatically generating cloze distractor is motivated. In
this paper, we investigate cloze distractor generation by exploring the
employment of pre-trained language models (PLMs) as an alternative for
candidate distractor generation. Experiments show that the PLM-enhanced model
brings a substantial performance improvement. Our best performing model
advances the state-of-the-art result from 14.94 to 34.17 (NDCG@10 score). Our
code and dataset is available at this https URL

本文通过探索预训练语言模型（PLMs）的应用作为备选项干扰项生成的替代方案，研究了填空干扰项的自动生成。实验证明，PLM 增强模型显著提高了性能，最佳模型使得 NDCG@10 得分从 14.94 提升至 34.17。我们的代码和数据集可在此链接中找到。

CDGP: 基于预训练语言模型的自动填空干扰项生成

CDGP: Automatic Cloze Distractor Generation based on Pre-trained  Language Model

In photo editing, it is common practice to remove visual distractions to
improve the overall image quality and highlight the primary subject. However,
manually selecting and removing these small and dense distracting regions can
be a laborious and time-consuming task. In this paper, we propose an
interactive distractor selection method that is optimized to achieve the task
with just a single click. Our method surpasses the precision and recall
achieved by the traditional method of running panoptic segmentation and then
selecting the segments containing the clicks. We also showcase how a
transformer-based module can be used to identify more distracting regions
similar to the user's click position. Our experiments demonstrate that the
model can effectively and accurately segment unknown distracting objects
interactively and in groups. By significantly simplifying the photo cleaning
and retouching process, our proposed model provides inspiration for exploring
rare object segmentation and group selection with a single click.

本研究提出了一种交互式干扰选择方法，通过单击一次来完成任务，可以有效准确地分割未知的干扰物体，并极大地简化照片清理和修饰过程。