The Technology of Translating Natural Language Merchandising Rules into Digital Planograms

Abstract

Objectives. The aim of the research is to develop and test an approach for the automatic transformation of product layout rules, formulated in natural language, into formalized machine-readable instructions to bridge the gap between business requirements and their technical implementation.

Methods. A hybrid approach is proposed in which a large language model performs the function of a semantic translator, converting a user query into a command in a specialized domain-specific language. The resulting    command is then processed by a deterministic parser based on regular expressions for validation and parameter extraction. The BLEU metric was used to evaluate the quality of the translation on a specially created dataset of 200 «query-reference» pairs. The effectiveness of the approach was compared with a baseline rule-based method.

Results. The experiment showed high accuracy in the formalization of queries compared to the baseline approach. A qualitative analysis confirmed the system's ability to interpret correctly synonyms and slang, extract implicitly specified parameters, and filter out irrelevant commands, which proves the robustness of the proposed approach. Conclusion. The conclusion is made about the viability and practical significance of the proposed approach for reducing labor intensity and increasing the efficiency of merchandising processes. The developed system      represents a foundation for creating a new generation of intelligent tools for managing retail space.