Multi-Stage Skill Selection for Natural Language Inputs
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Solution Overview
Problem
Current natural language processing systems face challenges in effectively selecting the most relevant skills to respond to user inputs across different languages and locales, leading to suboptimal user interactions.
Innovation Solution
A multi-stage, multi-lingual skill recommendation system that includes a skill candidates component and a skill ranking component to strategically select the best skill based on user locale, language, interaction history, and skill metadata, using search-based and model-based processing to improve relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a multi-stage skill recommendation system is implemented to improve skill selection accuracy across different languages and locales, then the relevance and accuracy of skill recommendations are improved, but the system complexity and processing time increase
Solution Approach 1:
The skill recommendation system is divided into multiple stages: a first stage that generates a broad set of candidate skills using search-based processing, and a second stage that refines these candidates using model-based processing. This segmentation allows the system to maintain high accuracy while managing complexity by handling different aspects of skill selection in separate, specialized components.
Solution Approach 2:
The system dynamically adapts its processing approach based on input characteristics. The multi-stage architecture enables the system to adjust the depth and type of processing applied to different queries, using search-based methods for initial candidate generation and model-based methods for final selection, optimizing both accuracy and computational efficiency.
2Adaptability or versatility
If multi-lingual and multi-locale support is added to improve user interaction across different languages and regions, then the adaptability and user satisfaction are improved, but the data processing requirements and system complexity increase
Solution Approach 1:
The system performs preliminary processing of natural language inputs to extract language and locale information before skill matching. By identifying the user's language and locale early in the processing pipeline, the system can efficiently filter and retrieve relevant skills from multi-lingual databases without processing all available data, reducing overall data processing requirements.
Solution Approach 2:
The patent introduces intermediate processing components that act as mediators between the raw natural language input and the skill database. These intermediaries include language detection modules, locale identification components, and skill matching algorithms that translate user inputs into appropriate skill queries, enabling efficient multi-lingual support without directly querying all skill databases.
3Reliability
If search-based and model-based processing are used to improve skill recommendation relevance, then the response accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The processing pipeline is segmented into two distinct stages: search-based processing that quickly generates candidate skills from the input, and model-based processing that refines these candidates to produce the final recommendation. This segmentation allows the system to leverage the speed of search-based methods for initial filtering while using model-based methods only on a reduced set of candidates, balancing accuracy and processing time.
Solution Approach 2:
The system applies model-based processing partially, only to the candidate skills generated in the first stage rather than to all possible skills. This partial application of computationally intensive processing reduces overall processing time while maintaining high accuracy, as the model only needs to evaluate a limited set of pre-filtered candidates.
Data Source
AI summary
Techniques for selecting a skill to execute in response to a natural language input are described. A system may receive a natural language input, determine profile data associated with the natural language input, and determine the profile data indicates a locale and at least first language and second languages. The system determines first and second sets of skills corresponding to the locale/first language and locale/second language, respectively. The system determines a first group of skill candidates corresponding to a portion of the first set of skills, and determines a second group of skill candidates corresponding to a portion of the second set of skills. The system performs ranking processing across the first and second groups of skills to determine a best skill for responding to the natural language input. Thus, in some situations, the skill invoked may not correspond to the same language represented in the natural language input.