论文标题
生成和重新划分：使用您的预测来改善语义解析的检索
Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing
论文作者
论文摘要
语义解析的最新方法通过检索和附加一组称为示例的训练样本来增强序列到序列模型。该配方的有效性受到检索有助于产生正确解析的信息示例的能力的限制，这在低资源环境中尤其具有挑战性。现有检索通常基于查询和示例输入的相似性。我们提出了Gandr，这是一种检索示例的检索程序，其输出也相似。 Gandrfirst通过基于输入的检索生成了初步预测。然后，它以类似于初步预测的输出来检索示例，该预测用于生成最终预测。甘德（Gandr）将艺术品设置为多个低资源语义解析任务。
A common recent approach to semantic parsing augments sequence-to-sequence models by retrieving and appending a set of training samples, called exemplars. The effectiveness of this recipe is limited by the ability to retrieve informative exemplars that help produce the correct parse, which is especially challenging in low-resource settings. Existing retrieval is commonly based on similarity of query and exemplar inputs. We propose GandR, a retrieval procedure that retrieves exemplars for which outputs are also similar. GandRfirst generates a preliminary prediction with input-based retrieval. Then, it retrieves exemplars with outputs similar to the preliminary prediction which are used to generate a final prediction. GandR sets the state of the art on multiple low-resource semantic parsing tasks.
