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生成,推理与排序:基于多任务架构的数学文字题生成(Generating, Reasoning & Ranking: Multitask Learning Framework for Math Word Problem Generation)
“数学文字题是一段能反映数学等式潜在逻辑的叙述性文本。成功的数学问题生成在语言生成和教育领域都具有广阔的应用前景。前人的工作大多需要人工标注的模板或关键词作为输入, 且未考虑数学表达式本身的特点。本文提出了一种多任务联合训练的问题文本生成模型。我们设计了三个辅助任务, 包括数字间关系抽取、数值排序和片段替换预测。他们与生成目标联合训练, 用以监督解码器的学习, 增强模型对运算逻辑和问题条件的感知能力。实验证明所提方法能有效提升生成的数学文字题的质量。”
Learning-based Models for Vulnerability Detection: An Extensive Study
Though many deep learning-based models have made great progress in vulnerability detection, we have no good understanding of these models, which limits the further advancement of model capability, understanding of the mechanism of model detection, and efficiency and safety of practical application of models.
DISK: Domain-constrained Instance Sketch for Math Word Problem Generation
A math word problem (MWP) is a coherent narrative which reflects the underlying logic of math equations.
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