# SParC: Cross-Domain Semantic Parsing in Context

## TL;DR

SParC is a challenging cross-domain semantic parsing dataset with complex contextual dependencies, designed to evaluate models' ability to generate SQL queries from multi-turn questions across diverse databases.

## Contribution

The paper introduces SParC, a new dataset for cross-domain, context-dependent semantic parsing, along with baseline experiments highlighting its complexity and challenges.

## Key findings

- State-of-the-art models achieve low accuracy on SParC.
- The dataset exhibits high semantic diversity and complex contextual dependencies.
- Cross-domain generalization remains a significant challenge.

## Abstract

We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It is obtained from controlled user interactions with 200 complex databases over 138 domains. We provide an in-depth analysis of SParC and show that it introduces new challenges compared to existing datasets. SParC demonstrates complex contextual dependencies, (2) has greater semantic diversity, and (3) requires generalization to unseen domains due to its cross-domain nature and the unseen databases at test time. We experiment with two state-of-the-art text-to-SQL models adapted to the context-dependent, cross-domain setup. The best model obtains an exact match accuracy of 20.2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research. The dataset, baselines, and leaderboard are released at https://yale-lily.github.io/sparc.