DAG Framework for Concurrent Chatbot Model Training
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Solution Overview
Problem
Conventional systems for training chatbots face challenges in determining user intents from natural language utterances due to the subtleties and ambiguity of language, making it difficult to improve chatbot performance without a flexible training approach.
Innovation Solution
A directed acyclic graph (DAG) based framework is used to build and train models for chatbots, allowing for the creation of pipelines with discrete tasks and relationships, enabling the execution of multiple models concurrently, with one model running in real-time and another in design-time for performance evaluation and improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single model is executed in run-time for chatbot operations, then real-time response capability is maintained, but performance improvement and accuracy enhancement are limited
Solution Approach 1:
The system executes a second model in design-time (before deployment) to perform preliminary performance evaluation and training. This allows the model to be improved in advance without affecting real-time chatbot operations. The analytic engine evaluates the second model's performance using collected attributes and metrics, and the improved model is then deployed to replace the first model in run-time.
2Reliability
If multiple models are trained and evaluated, then performance improvement is achieved, but system complexity and computational resources increase
Solution Approach 1:
The system divides model execution into two distinct segments: a first model executed in run-time for real-time chatbot operations, and a second model executed in design-time for performance evaluation and improvement. This segmentation allows independent optimization of each model's purpose - one for speed and real-time response, the other for accuracy and performance testing - while maintaining clear separation of concerns through the DAG framework.
Solution Approach 2:
The analytic engine serves as an intermediary between the two models and the event collector. It collects attributes from the event collector, evaluates both models using these attributes and defined metrics, determines which model performs better, and manages the deployment decisions. This intermediary component simplifies model management by centralizing the evaluation and selection logic.
3Ease of manufacture
If conventional training approaches are used, then implementation is straightforward, but ability to handle language subtleties and ambiguity is insufficient
Solution Approach 1:
The system implements dynamic model evaluation and replacement based on performance metrics. Instead of using a static, fixed training approach, the system continuously collects attributes from real chatbot operations, evaluates model performance dynamically using the analytic engine, and replaces models based on their actual performance in handling user intents. This dynamic approach allows the system to adapt to language subtleties and ambiguity by learning from real operational data.
Data Source
AI summary
Techniques for chatbots, and more particularly, to techniques for using a directed acyclic graph (DAG) based framework to build and train models. In one particular aspect, a computer implemented method is provided that includes generating, by a DAG based framework, a first model and a second model, executing the first model for a chatbot in run-time and second model for the chatbot in design-time, collecting attributes for intent classification associated with a set of utterances with the chatbot running the first model and the second model, evaluating, using one or more metrics, performance of the first model and the second model based on an analysis of the attributes for the intent classification, determining whether the performance of the second model is improved as compared to the performance of the first model, and executing the first model or the second model for the chatbot in run-time based on the performance determination.