Error-Aware Interactive Semantic Parsing of OpenStreetMap

TL;DR

This paper introduces an error-aware interactive semantic parsing method for OpenStreetMap queries, which detects ambiguities, asks clarifying questions, and improves parsing accuracy through user interaction.

Contribution

It presents a novel approach combining error detection, clarification questions, and multi-source training to enhance semantic parsing accuracy in geographical queries.

Findings

Achieved a 1.2% F1 score improvement on NLMaps dataset.

Integrated entropy-based uncertainty detection with beam search.

Demonstrated effectiveness of interactive clarification in semantic parsing.

Abstract

In semantic parsing of geographical queries against real-world databases such as OpenStreetMap (OSM), unique correct answers do not necessarily exist. Instead, the truth might be lying in the eye of the user, who needs to enter an interactive setup where ambiguities can be resolved and parsing mistakes can be corrected. Our work presents an approach to interactive semantic parsing where an explicit error detection is performed, and a clarification question is generated that pinpoints the suspected source of ambiguity or error and communicates it to the human user. Our experimental results show that a combination of entropy-based uncertainty detection and beam search, together with multi-source training on clarification question, initial parse, and user answer, results in improvements of 1.2% F1 score on a parser that already performs at 90.26% on the NLMaps dataset for OSM semantic parsing.