字里行间：关于为何某些文本比其他文本更难读的数据集与研究
Reading Between the Lines: A dataset and a study on why some texts are tougher than others
AI总结：
本文针对智力障碍读者的文本可读性问题，构建了基于平行语料的困难标注数据集，并微调transformer模型预测简化策略，同时探索模型决策的可解释性。
摘要：
我们的研究旨在更好地理解是什么使文本对特定受众——即智力障碍人士，更具体地说，是在认知功能（如阅读和理解能力）方面存在局限、智商低于70且在概念领域面临挑战的人群——难以阅读。我们引入了一种基于心理学实证研究以及翻译研究中的研究成果的困难标注方案。本文描述了该标注数据集，该数据集主要来源于在线提供的平行文本（标准英语与易读英语翻译）。我们微调了四个不同的预训练transformer模型来执行多分类任务，以预测简化所需的策略。我们还研究了当该语言模型旨在预测句子难度时，解释其决策的可能性。相关资源可从 https://github.com/Nouran-Khallaf/why-tough 获取。
英文摘要：
Our research aims at better understanding what makes a text difficult to read for specific audiences with intellectual disabilities, more specifically, people who have limitations in cognitive functioning, such as reading and understanding skills, an IQ below 70, and challenges in conceptual domains. We introduce a scheme for the annotation of difficulties which is based on empirical research in psychology as well as on research in translation studies. The paper describes the annotated dataset, primarily derived from the parallel texts (standard English and Easy to Read English translations) made available online. we fine-tuned four different pre-trained transformer models to perform the task of multiclass classification to predict the strategies required for simplification. We also investigate the possibility to interpret the decisions of this language model when it is aimed at predicting the difficulty of sentences. The resources are available from https://github.com/Nouran-Khallaf/why-tough
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
