论文标题
在语义解析中进行域概括的元学习
Meta-Learning for Domain Generalization in Semantic Parsing
论文作者
论文摘要
长期以来，人们已经确认了建立语义解析器的重要性，该语义解析器可以应用于新领域并在培训中产生未见的程序，并且越来越多地可用的数据集测试。但是，很少或没有任何关注致力于学习促进领域概括的算法或目标，几乎所有现有的方法都依赖于标准的监督学习。在这项工作中，我们使用一个元学习框架，该框架针对零击域的概括进行语义解析。我们应用模型不足的训练算法，该算法通过构造虚拟列车和测试集来模拟零射门解析。学习目标利用了直觉，即改善源域性能的梯度步骤也应提高目标域的性能，从而鼓励解析器概括以看不见目标域。 （英语）蜘蛛和中国蜘蛛数据集的实验结果表明，元学习目标显着提高了基线解析器的性能。
The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing out-of-domain performance are becoming increasingly available. However, little or no attention has been devoted to learning algorithms or objectives which promote domain generalization, with virtually all existing approaches relying on standard supervised learning. In this work, we use a meta-learning framework which targets zero-shot domain generalization for semantic parsing. We apply a model-agnostic training algorithm that simulates zero-shot parsing by constructing virtual train and test sets from disjoint domains. The learning objective capitalizes on the intuition that gradient steps that improve source-domain performance should also improve target-domain performance, thus encouraging a parser to generalize to unseen target domains. Experimental results on the (English) Spider and Chinese Spider datasets show that the meta-learning objective significantly boosts the performance of a baseline parser.
