Interactive semantic parsing based on natural language (NL) feedback, where
users provide feedback to correct the parser mistakes, has emerged as a more
practical scenario than the traditional one-shot semantic parsing. However,
prior work has heavily relied on human-annotated feedback data to train the
interactive semantic parser, which is prohibitively expensive and not scalable.
In this work, we propose a new task of simulating NL feedback for interactive
semantic parsing. We accompany the task with a novel feedback evaluator. The
evaluator is specifically designed to assess the quality of the simulated
feedback, based on which we decide the best feedback simulator from our
proposed variants. On a text-to-SQL dataset, we show that our feedback
simulator can generate high-quality NL feedback to boost the error correction
ability of a specific parser. In low-data settings, our feedback simulator can
help achieve comparable error correction performance as trained using the
costly, full set of human annotations.

提出了一种利用模拟的自然语言反馈来训练交互式语义解析器的任务和新颖的反馈评估器，证明该反馈模拟器可以在低数据集情况下，生成高质量的自然语言反馈来提高特定解析器的错误纠正能力。

学习模拟自然语言反馈进行交互式语义解析

Learning to Simulate Natural Language Feedback for Interactive Semantic  Parsing

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。