PleaSQLarify: Visual Pragmatic Repair for Natural Language Database Querying

TL;DR

PleaSQLarify introduces a pragmatic repair approach with a visual interface for clarifying ambiguous natural language database queries, improving user control and interpretation accuracy.

Contribution

It operationalizes pragmatic repair through interpretable decision variables and a visual interface, enabling efficient clarification in natural language database querying.

Findings

Enhanced user understanding of query interpretations

Efficient ambiguity resolution through minimal interaction

Pragmatic repair improves natural language interface control

Abstract

Natural language database interfaces broaden data access, yet they remain brittle under input ambiguity. Standard approaches often collapse uncertainty into a single query, offering little support for mismatches between user intent and system interpretation. We reframe this challenge through pragmatic inference: while users economize expressions, systems operate on priors over the action space that may not align with the users'. In this view, pragmatic repair -- incremental clarification through minimal interaction -- is a natural strategy for resolving underspecification. We present \textsc{PleaSQLarify}, which operationalizes pragmatic repair by structuring interaction around interpretable decision variables that enable efficient clarification. A visual interface complements this by surfacing the action space for exploration, requesting user disambiguation, and making belief updates traceable across turns. In a study with twelve participants, \textsc{PleaSQLarify} helped users recognize alternative interpretations and efficiently resolve ambiguity. Our findings highlight pragmatic repair as a design principle that fosters effective user control in natural language interfaces.