Modeling crisp logical regularities is crucial in semantic parsing, making it difficult for neural models with no task-specific prior knowledge to achieve good results. In this paper, we introduce data recombination, a novel framework for injecting such prior knowledge into a model. From the training data, we induce a high-precision synchronous context-free grammar, which captures important conditional independence properties commonly found in semantic parsing. We then train a sequence-to-sequence recurrent network (RNN) model with a novel attention-based copying mechanism on datapoints sampled from this grammar, thereby teaching the model about these structural properties. Data recombination improves the accuracy of our RNN model on three semantic parsing datasets, leading to new state-of-the-art performance on the standard GeoQuery dataset for models with comparable supervision.

本研究介绍了一种新的数据再组合框架，该框架可以将先前的领域知识注入到神经模型中，以提高模型的准确性。通过从训练数据中引出高精度同步上下文无关文法，并用新颖的基于注意力机制的复制方法，在利用语法生成的样本数据上训练序列到序列的循环神经网络模型（RNN），帮助模型学习感知句法。数据再组合提高了我们的RNN模型在三个语义解析数据集上的准确性，在使用类似的监督数据输出下，在标准的GeoQuery数据集上比以往模型表现更具优势。

神经语义解析的数据重组