As a promising paradigm, interactive semantic parsing has shown to improve
both semantic parsing accuracy and user confidence in the results. In this
paper, we propose a new, unified formulation of the interactive semantic
parsing problem, where the goal is to design a model-based intelligent agent.
The agent maintains its own state as the current predicted semantic parse,
decides whether and where human intervention is needed, and generates a
clarification question in natural language. A key part of the agent is a world
model: it takes a percept (either an initial question or subsequent feedback
from the user) and transitions to a new state. We then propose a simple yet
remarkably effective instantiation of our framework, demonstrated on two
text-to-SQL datasets (WikiSQL and Spider) with different state-of-the-art base
semantic parsers. Compared to an existing interactive semantic parsing approach
that treats the base parser as a black box, our approach solicits less user
feedback but yields higher run-time accuracy.

本论文提出了一个新的交互式语义解析问题的统一形式，其中的目标是设计一个基于模型的智能代理。代理能够自主决定是否和何时需要人类干预，并生成自然语言的澄清问题，使用了世界模型并在两个 Text-to-SQL 数据集上得到了很好的效果。

基于模型的交互式语义解析：统一框架和文本到 SQL 的案例研究

Model-based Interactive Semantic Parsing: A Unified Framework and A  Text-to-SQL Case Study

The sequence-to-sequence paradigm employed by neural text-to-SQL models
typically performs token-level decoding and does not consider generating SQL
hierarchically from a grammar. Grammar-based decoding has shown significant
improvements for other semantic parsing tasks, but SQL and other general
programming languages have complexities not present in logical formalisms that
make writing hierarchical grammars difficult. We introduce techniques to handle
these complexities, showing how to construct a schema-dependent grammar with
minimal over-generation. We analyze these techniques on ATIS and Spider, two
challenging text-to-SQL datasets, demonstrating that they yield 14--18\%
relative reductions in error.

本文提出了一种处理 neural text-to-SQL models 中编写语法的复杂性的技术，从而生成 schema-dependent grammar，对 ATIS 和 Spider 两个挑战性的 text-to-SQL 数据集进行分析，显示可以减少 14-18% 的相对误差。