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首页> 外文会议>Annual Meeting of the Association for Computational Linguistics;International Joint Conference on natural Language Processing >Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children's mindreading ability
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Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children's mindreading ability

机译：矢量可以比专家更好地阅读思想吗？ 比较儿童思维能力自动评分的数据增强策略

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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-Fl-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.
机译：在本文中，我们实施并比较了7个不同的数据增强策略，以便为儿童了解其他人的思想，感受和欲望（或“Mindreading”）的自动评分任务。我们招募域名专家以重新注释增强样本，并确定各种策略保留原始评级的程度。我们还对多次实验进行了多次实验，以衡量每个增强策略提高自动评分系统性能的程度。为了确定自动系统的能力概括到未看见的数据，我们创建了英国思维 - 20 - 一种新的儿童表现的思维表现，包括10,320个问题答案对。我们在Mind-CA语料库中获得了新的最先进的性能，提高了6分的宏观分数。结果表明，增强策略的训练示例的数量和增强策略的质量影响了系统的性能。任务特定的增强通常优于任务不可知的增强。基于矢量（手套，FastText）的自动增强表现最差。我们发现在Mind-CA上培训的系统概括为英国思维 - 20。我们证明数据增强策略还提高了看不见数据的性能。

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