Semantic Parsing Models and Context Awareness for Instruction Following in Enterprise Helpdesk Systems

Abstract

The rapid automation of enterprise operations has positioned intelligent helpdesk systems as critical components for managing internal IT workflows, human resource inquiries, and customer support services. A fundamental challenge in designing these systems is ensuring reliable and accurate instruction following, which requires the translation of unstructured natural language queries into executable machine commands. This paper presents a comprehensive examination of how semantic parsing models, when augmented with sophisticated context awareness mechanisms, facilitate highly accurate instruction following in enterprise helpdesk environments. By mapping complex multi-turn dialogue utterances into formal meaning representations, semantic parsers bridge the semantic gap between human intent and system execution. We explore the architectural considerations necessary for deploying these models in specialized enterprise domains, focusing on the interplay between dialogue state tracking, coreference resolution, and domain-specific knowledge integration. Extensive qualitative and quantitative analyses reveal that isolated semantic parsing is insufficient for complex enterprise workflows without a robust contextual framework capable of resolving ambiguities over long conversation trajectories. Through a detailed investigation of system performance metrics, error taxonomies, and architectural design patterns, this research elucidates the mechanisms by which contextually grounded parsers improve command execution accuracy. The findings demonstrate that integrating historical dialogue context directly into the decoding phase of semantic parsing models significantly reduces execution errors and enhances user satisfaction, providing a robust theoretical foundation for future advancements in enterprise conversational agents.