带菌者能比专家更能读懂人的心思吗？儿童读心能力自动评分的数据增强策略比较
Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children's mindreading ability
摘要：
在本文中，我们实现和比较7个不同的数据增强策略的任务，自动评分的儿童的能力，了解别人的想法，感受和愿望（或“读心术”）。我们招募了领域内的专家来重新注释增强的样本，并确定每个策略在多大程度上保留了原始评级。我们还进行了多个实验，以衡量每种增强策略在多大程度上提高了自动评分系统的性能。为了确定自动系统的能力，以概括看不见的数据，我们创建UK-MIND-20 -一个新的语料库的儿童的表现测试的读心术，由10,320个问题，答案对。我们在MIND-CA语料库上获得了新的最先进的性能，将宏观F1分数提高了6分。结果表明，训练样本的数量和增强策略的质量都会影响系统的性能。任务特定的增强通常优于任务不可知的增强。基于向量的自动增强（GloVe，FastText）表现最差。我们发现，在MIND-CA上训练的系统可以很好地推广到UK-MIND-20。我们证明了数据增强策略也提高了看不见的数据的性能。
英文摘要：
In this paper we implement and compare 7 different data augmentation strategies for the task of automatic scoring of children's ability to understand others' thoughts, feelings, and desires (or "mindreading"). We recruit in-domain experts to re-annotate augmented samples and determine to what extent each strategy preserves the original rating. We also carry out multiple experiments to measure how much each augmentation strategy improves the performance of automatic scoring systems. To determine the capabilities of automatic systems to generalize to unseen data, we create UK-MIND-20 - a new corpus of children's performance on tests of mindreading, consisting of 10,320 question-answer pairs. We obtain a new state-of-the-art performance on the MIND-CA corpus, improving macro-F1-score by 6 points. Results indicate that both the number of training examples and the quality of the augmentation strategies affect the performance of the systems. The task-specific augmentations generally outperform task-agnostic augmentations. Automatic augmentations based on vectors (GloVe, FastText) perform the worst. We find that systems trained on MIND-CA generalize well to UK-MIND-20. We demonstrate that data augmentation strategies also improve the performance on unseen data.
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。
