Slot Type Resolution in NLU Systems
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Solution Overview
Problem
Current systems face inefficiencies in hosting and scaling natural language understanding (NLU) models due to resource wastage, as many models remain idle, and existing technologies struggle with complex slot type resolution and data transformation in conversational interfaces.
Innovation Solution
Implementing a distributed, multi-tenant hosting system with auto-scaling capabilities for NLU models, utilizing composite slot types and graph/tree structures for slot value resolution, and leveraging FSTs and DNNs for efficient inference operations, along with a grammar DSL for training data generation and slot type definition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If NLU models are hosted using traditional single-tenant systems, then each model gets dedicated resources, but resource wastage occurs when models remain idle
Solution Approach 1:
The patent combines multiple NLU models and their computational resources into a single shared hosting infrastructure. Multiple tenants share the same physical or virtualized resources, allowing dynamic allocation and utilization of computing power, memory, and storage across different models, thereby eliminating idle resource wastage while maintaining model availability.
Solution Approach 2:
The system implements dynamic resource allocation where computing resources are not statically assigned but dynamically adjusted based on real-time demand. When certain NLU models are actively processing requests, they receive allocated resources; when idle, resources are automatically reallocated to other active models, creating a flexible and efficient multi-tenant environment.
2Measurement precision
If complex slot type resolution is implemented using traditional methods, then accurate intent recognition is achieved, but processing time and computational complexity increase
Solution Approach 1:
The slot type resolution process is segmented into multiple independent stages: entity recognition, slot filling, and intent classification. Each stage processes specific aspects of the input independently, allowing parallel computation and reducing overall processing time while maintaining accurate resolution of complex slot types through specialized sub-processes.
Solution Approach 2:
The patent introduces an intermediary layer of pre-trained language models and contextual embeddings that bridge raw input text and final slot type resolution. This intermediary representation captures semantic meaning and contextual relationships, enabling accurate slot type identification without requiring complex rule-based processing, thereby reducing computational complexity and processing time.
3Adaptability or versatility
If more NLU models are hosted to improve service coverage, then conversational interface capabilities increase, but infrastructure cost and resource management complexity increase
Solution Approach 1:
The hosting infrastructure is designed as a universal platform that can simultaneously host and manage multiple different NLU models with varying requirements. The system provides model-agnostic resource allocation, unified deployment interfaces, and standardized management protocols, allowing diverse models to coexist on the same infrastructure without increasing management complexity, thereby improving service coverage efficiently.
Data Source
AI summary
Techniques to be used in natural language understanding (NLU) are described. For example, a NLU service to receive a request to analyze a written or spoken utterance; tokenize the received utterance; generate one or more labels corresponding to a substring of the tokenized received utterance, each of the labels including one or more slot types, by: for each path of a grammar-based finite state transducer (FST) data structure that includes instructions, traversing the path as far as possible for matches from a previous breakpoint, while maintaining i) locations of branching points and snapshots at those branching points and ii) an indication of which paths have been traversed, and recording a result of each path traversal as a generated label; resolve the one or more generated labels into machine-readable values; and output a result is described.