Enterprise AI Analysis
From Menus to Interactive Food-Ordering Systems
Min-Ji Kim, Seong-Jin Park, Jaehwan Ha, Ju-Won Seo, Dinara Aliyeva, Kang-Min Kim
This study proposes a fully automated, end-to-end framework for building voice-based conversational interfaces in food-ordering kiosks. Our approach transforms structured menu databases into high-quality annotated datasets and efficiently deploys store-specific conversational models using a parameter-efficient fine-tuning method, requiring only 0.9% of the backbone model parameters per store. We integrate a recommendation module that suggests alternative items when requested menu options are unavailable. Experimental results on data from 27 stores in South Korea demonstrate consistent outperformance against existing baselines in intent classification and slot filling, while maintaining high annotation quality. Simulated real-world voice-ordering scenarios confirm the practicality of our framework for rapid, scalable, and accessible deployment in real-world environments.
Keywords: Natural Language Understanding, Pre-trained Language Model, Automatic Framework, Conversational Interface, Food Ordering System, Accessibility Systems
Key Business Impact
Leveraging advanced NLU and efficient deployment strategies for scalable, accessible food-ordering systems.
0.9%
Parameters Fine-tuned Per Store
96.11%
IC Accuracy in Real-World Simulation
88%
Recommendation Hit@1
Deep Analysis & Enterprise Applications
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Automated & Robust Data Generation
Our framework employs a template-based approach to automatically construct high-quality, store-specific training datasets for Intent Classification (IC) and Slot Filling (SF) from structured menu databases. This process eliminates the need for costly manual annotation, ensuring complete slot coverage and natural language fluency.
Key techniques include attribute expression refinement to mitigate STT errors, heuristic replacement of special characters (e.g., '&' to 'and'), and character-level perturbations (random space insertion) to simulate disfluent speech and enhance robustness in sub-utterance modeling. This method significantly outperforms baselines in both efficiency and data quality for NLU tasks.
Efficient Model Training & Scalable Deployment
To support multiple stores with diverse menus, we adopt a parameter-efficient adapter tuning strategy. Store-specific P-Adapters are fine-tuned on a shared backbone model, modifying only a small fraction of parameters (0.9% per store). This design enables plug-and-play extensibility: new store adapters can be added without retraining the entire model, and obsolete ones removed without affecting system integrity.
This approach significantly reduces memory and compute overhead, making the deployment of voice-ordering systems highly scalable and cost-effective across various store environments. Multitask learning for IC and SF is applied during adapter fine-tuning, ensuring robust performance.
Real-Time Service & Intelligent Recommendation
The framework integrates a recommendation module within the real-time serving pipeline to enhance user experience. When a user requests an unavailable menu item, the system detects low-confidence predictions using a softmax function and predefined thresholds (Tconf).
Upon detection of uncertainty, the module computes cosine similarity using TF-IDF vectors between the user's utterance and all available menu items to suggest plausible alternatives. This intelligent fallback mechanism improves system robustness, reduces user frustration from "item not found" errors, and supports higher order completion rates in dynamic voice-ordering environments.
Enterprise Process Flow: End-to-End Framework
Structured Menu Database Input
→
Store-Specific Dataset Generation
→
Adapter-based Model Training (IC & SF)
→
Unified Model Deployment with Recommendation
→
Real-Time Voice-Ordering Service
0.9%
of backbone model parameters are fine-tuned per store using P-Adapters, ensuring highly efficient and scalable deployment across diverse environments.
| Method / Metric | Intent Acc (%) | Slot F1E (%) | Slot F1C (%) |
|---|---|---|---|
| TUDA | 97.54 | 82.02 | 91.61 |
| Bllossom | 93.44 | 77.39 | 88.24 |
| Ours (P-Adapter) | 97.52 | 89.22 | 94.62 |
88% (Hit@1)
and 96% Hit@5 for unavailable item recommendations, ensuring high reliability and enhanced user experience in conversational ordering.
Case Study: Real-World Performance in Voice-Ordering
Our framework was validated in a simulated voice-ordering kiosk environment across 27 stores in South Korea. Using human participants and a robust STT model (Whisper-large-v3) for transcription, the system demonstrated exceptional real-world applicability.
Achieving a 96.11% Intent Classification accuracy and 84.06% Slot Filling accuracy, the framework proves its practical readiness for scalable and accessible deployment. This confirms the robustness of our data generation and adapter-based model training under realistic input conditions, making conversational AI a viable solution for everyday commerce.
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Annual Cost Savings
$52,000
Annual Hours Reclaimed
1,040
Your AI Implementation Roadmap
A typical phased approach to integrate conversational AI, tailored for enterprise adoption.
Phase 01: Discovery & Strategy
Comprehensive analysis of existing menu structures, operational workflows, and specific accessibility requirements. Define clear AI objectives and success metrics for automated ordering systems.
Phase 02: Data Automation & Model Training
Automate dataset generation from menu databases, applying advanced augmentation. Deploy and fine-tune store-specific P-Adapters on a shared NLU backbone model.
Phase 03: System Integration & Testing
Integrate the conversational interface with existing POS systems and STT modules. Conduct rigorous testing, including real-world simulations and user acceptance testing, for intent classification, slot filling, and recommendation accuracy.
Phase 04: Deployment & Optimization
Roll out the voice-ordering system across target stores. Continuously monitor performance, gather user feedback, and refine models and recommendation logic for ongoing optimization and scalability.
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