In this paper, we present an approach to incorporate retrieved datapoints as
supporting evidence for context-dependent semantic parsing, such as generating
source code conditioned on the class environment. Our approach naturally
combines a retrieval model and a meta-learner, where the former learns to find
similar datapoints from the training data, and the latter considers retrieved
datapoints as a pseudo task for fast adaptation. Specifically, our retriever is
a context-aware encoder-decoder model with a latent variable which takes
context environment into consideration, and our meta-learner learns to utilize
retrieved datapoints in a model-agnostic meta-learning paradigm for fast
adaptation. We conduct experiments on CONCODE and CSQA datasets, where the
context refers to class environment in JAVA codes and conversational history,
respectively. We use sequence-to-action model as the base semantic parser,
which performs the state-of-the-art accuracy on both datasets. Results show
that both the context-aware retriever and the meta-learning strategy improve
accuracy, and our approach performs better than retrieve-and-edit baselines.

本文提出一种方法，利用检索到的数据点作为上下文依赖的语义解析的支持证据，通过结合检索模型和元学习器实现。在 CONCODE 和 CSQA 数据集上的实验表明，这种方法在 JAVA 代码和对话历史上的环境下，都可以提高准确性，比检索和编辑的基线方法更有效。