IR Design for Application-Specific Natural Language: A Case Study on Traffic Data

TL;DR

This paper proposes an intermediate representation (IR) design for Application-Specific Natural Language in transportation, significantly enhancing data processing speed by converting transportation data into graph format, thus addressing complexity issues.

Contribution

It introduces a novel IR design tailored for ASNL in transportation, enabling efficient data processing and outperforming standard XML data formats in query speed.

Findings

Over 40x speed improvement in data query operations

Effective processing of transportation data into graph format

Enhanced performance for application-specific natural language processing

Abstract

In the realm of software applications in the transportation industry, Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their ease of use and various other benefits. With the ceaseless progress in computer performance and the rapid development of large-scale models, the possibility of programming using natural language in specified applications - referred to as Application-Specific Natural Language (ASNL) - has emerged. ASNL exhibits greater flexibility and freedom, which, in turn, leads to an increase in computational complexity for parsing and a decrease in processing performance. To tackle this issue, our paper advances a design for an intermediate representation (IR) that caters to ASNL and can uniformly process transportation data into graph data format, improving data processing performance. Experimental comparisons reveal that in standard data query operations, our proposed IR design can achieve a speed improvement of over forty times compared to direct usage of standard XML format data.