Multi-factor NLP Modeling for Chatbot Skill Selection
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Solution Overview
Problem
Current chatbot systems face inefficiencies in selecting the appropriate skill to handle customer inquiries due to the failure of single-factor machine learning models to account for contextual complexities in natural language processing, leading to incorrect skill selection and decreased performance.
Innovation Solution
The implementation of multi-factor modeling techniques that utilize an intent classifier model and a transform mask model to determine the most appropriate skill for processing utterances, incorporating contextual analysis and intent classification to improve skill selection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If single-factor machine learning models are used for skill selection, then prediction time efficiency is improved, but skill selection accuracy deteriorates due to failure to account for contextual complexities
Solution Approach 1:
The patent segments the skill selection process into multiple independent factors including intent classification, entity recognition, and contextual analysis. Each factor is processed separately through dedicated machine learning models, allowing the system to evaluate multiple dimensions of the utterance without compromising time efficiency. This segmentation enables accurate skill selection by considering contextual complexities while maintaining computational efficiency through modular processing.
2Device complexity
If direct skill selection mapping is used, then model complexity is reduced, but contextual analysis capability deteriorates
Solution Approach 1:
The patent introduces intermediate processing layers between the input utterance and the final skill selection. These intermediaries include intent classification models, entity recognition components, and contextual feature extractors that bridge the gap between simple input and complex skill determination. This intermediary approach enables sophisticated contextual analysis without requiring a single monolithic complex model, thus maintaining manageable system complexity while enhancing analytical capability.
3Loss of time
If single-factor models are used for skill prediction, then training time is reduced, but performance deteriorates due to insufficient contextual learning
Solution Approach 1:
The patent applies preliminary action by pre-training multiple specialized models on specific aspects of the data (intent classification, entity recognition, contextual features) before combining their outputs for skill selection. This pre-processing approach allows each model to specialize in particular contextual elements, improving overall performance. The training is divided into stages where foundational models are trained first, then integrated, reducing total training time while achieving superior performance compared to training a single comprehensive model.
Data Source
AI summary
Techniques are disclosed for systems including techniques for multi-factor modelling for training and utilizing chatbot systems for natural language processing. In an embodiment, a method includes receiving a set of utterance data corresponding to a natural language-based query, determining one or more intents for the chatbot corresponds to a possible context for the natural language-based query and associated with a skill for the chatbot, generating one or more intent classification datasets, each intent classification dataset associated with a probability that the natural language query corresponds to an intent of the one or more intents, generating one or more transformed datasets each corresponding to a skill of one or more skills, determining a first skill of the one or more skills based on the one or more transformed datasets and processing, based on the determined first skill, the set of utterance data to resolve the natural language-based query.