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

arXiv: https://arxiv.org/abs/2307.06983 · HTML: https://mlsanity.com/paper/2307.06983/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation](https://mlsanity.com/paper/1905.08205/) (2019)
- [Compiler Support for Sparse Tensor Computations in MLIR](https://mlsanity.com/paper/2202.04305/) (2022)
- [Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning](https://mlsanity.com/paper/2303.13986/) (2023)

## Built on by (most cited first, 1 of 1)

- [Evolutionary City: Towards a Flexible, Agile and Symbiotic System](https://mlsanity.com/paper/2311.14690/) (2023)

## Abstract (arXiv)

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.