Natural Language Inference Model for Intent Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional machine learning models for analyzing natural language require large amounts of labeled training data, making them costly and inefficient for determining user intent in chatbot interactions.
Innovation Solution
The system uses a natural language inference model with a small amount of labeled training data, matching user inputs with stored examples to determine intent, and employs a confidence threshold to handle out-of-scope intents, allowing for efficient intent determination without extensive human labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used for analyzing natural language, then prediction quality can be achieved, but large amounts of labeled training data are required which increases cost and time
Solution Approach 1:
The patent uses a pre-trained language model (such as BERT) that has been copied from general-domain training to handle intent classification. This pre-trained model serves as a foundation that can be fine-tuned with much smaller domain-specific datasets, avoiding the need to train from scratch with large amounts of labeled data.
Solution Approach 2:
The patent applies parameter changes by fine-tuning the pre-trained language model on domain-specific data. The model's parameters are adjusted through training on a smaller dataset of labeled intent examples, allowing the model to adapt to specific domains while maintaining the benefits of the pre-trained architecture.
2Measurement precision
If human experts label training data, then model accuracy improves, but the process is costly in terms of time and money
Solution Approach 1:
The patent leverages a pre-trained language model that has already been trained on extensive general-domain data, effectively copying the knowledge and linguistic understanding from that pre-training. This allows the system to achieve high accuracy on intent classification without requiring human experts to label large amounts of training data from scratch.
Solution Approach 2:
The patent performs preliminary action by using a pre-trained language model that has already completed the expensive and time-consuming task of learning from large datasets during its pre-training phase. This preliminary training is done once and can be fine-tuned for specific domains with much smaller datasets, avoiding repeated human labeling efforts.
3Measurement precision
If more training data is collected, then model prediction quality improves, but the cost and complexity of the system increases
Solution Approach 1:
The patent uses a pre-trained language model architecture that has been copied and adapted for intent classification. This pre-trained model handles the complexity of processing natural language through its pre-learned representations, allowing the system to achieve good prediction quality without collecting and processing large amounts of training data, thus reducing overall system complexity.
Data Source
AI summary
An online system allows user interactions using natural language expressions. The online system uses a machine learning based model to infer an intent represented by a user expression. The machine learning based model takes as input a user expression and an example expression to compute a score indicating whether the user expression matches the example expression. Based on the scores, the intent inference module determines a most applicable intent for the expression. The online system determines a confidence threshold such that user expressions indicating a high confidence are assigned the most applicable intent and user expressions indicating a low confidence are assigned an out-of-scope intent. The online system encodes the example expressions using the machine learning based model. The online system may compare an encoded user expression with encoded example expressions to identify a subset of example expressions used to determine the most applicable intent.