论文标题
常识性推理的生成数据增强
Generative Data Augmentation for Commonsense Reasoning
论文作者
论文摘要
常识性推理的最新进展取决于大规模的人类注销训练数据，以达到峰值性能。但是，训练示例的手动策划很昂贵，并且已被证明会引入神经模型可以轻易利用和过度拟合的注释伪像。我们研究了G-Daug^C，这是一种新型生成数据增强方法，旨在在低资源环境中实现更准确，更健壮的学习。我们的方法使用验证的语言模型生成综合示例，并选择最有用，最多样化的示例以进行数据增强。在具有多个常识性推理基准测试的实验中，G-Daug^c始终优于基于反向翻译的现有数据增强方法，并建立了有关Winogrande，Codah和Commonsenseqa的新最新方法。此外，除了提高分配准确性外，G-dauge^caugment训练还增强了分布的概括，显示出针对对抗性或扰动示例的更大鲁棒性。我们的分析表明，G-Daug^C产生了多种流利的培训示例，其选择和训练方法对于性能很重要。我们的发现鼓励未来的研究对生成数据的增强，以增强分布学习和分布概括。
Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance. However, manual curation of training examples is expensive and has been shown to introduce annotation artifacts that neural models can readily exploit and overfit on. We investigate G-DAUG^C, a novel generative data augmentation method that aims to achieve more accurate and robust learning in the low-resource setting. Our approach generates synthetic examples using pretrained language models, and selects the most informative and diverse set of examples for data augmentation. In experiments with multiple commonsense reasoning benchmarks, G-DAUG^C consistently outperforms existing data augmentation methods based on back-translation, and establishes a new state-of-the-art on WinoGrande, CODAH, and CommonsenseQA. Further, in addition to improvements in in-distribution accuracy, G-DAUG^C-augmented training also enhances out-of-distribution generalization, showing greater robustness against adversarial or perturbed examples. Our analysis demonstrates that G-DAUG^C produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance. Our findings encourage future research toward generative data augmentation to enhance both in-distribution learning and out-of-distribution generalization.
