Vehicle FAQ Intent Recognition Using Multi-Task Learning
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Solution Overview
Problem
Existing FAQ systems struggle to accurately distinguish between questions with similar meanings, leading to incorrect answers due to unclear distinctions between them.
Innovation Solution
A question-and-answer system for vehicles that employs multi-task learning with deep learning models, performing global and sequential encoding, and contrastive learning to identify user intentions and provide accurate responses by adjusting weights based on loss values calculated from input and sample data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional FAQ system is used to provide pre-set question-answer pairs, then the system is simple and easy to implement, but it cannot clearly distinguish between questions with similar meanings, resulting in incorrect answers
Solution Approach 1:
The patent transforms the classification approach by changing from traditional keyword-matching parameters to deep learning model parameters including global encoding vectors and bidirectional sequence encoding vectors. This allows the system to capture semantic nuances and distinguish between similar questions effectively.
Solution Approach 2:
The patent segments the question classification task into multiple encoding stages: global encoding to capture overall question meaning, bidirectional encoding to capture contextual relationships, and multi-task learning to handle different classification objectives simultaneously. This segmentation improves distinction accuracy while managing complexity.
2Measurement precision
If multi-task learning with deep learning models is implemented to improve question classification accuracy, then the system can distinguish similar questions better, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary global encoding on input questions before detailed bidirectional sequence encoding. This preliminary action extracts key features early, reducing the computational burden of subsequent processing and enabling faster identification of user intentions while maintaining high accuracy.
Solution Approach 2:
The patent implements multi-task learning where a single deep learning model performs multiple classification tasks simultaneously (e.g., question type classification, intent recognition, answer selection). This multi-functionality improves identification accuracy while reducing overall processing time compared to multiple separate models.
Data Source
AI summary
A system for a vehicle is provided. The system may include a wireless interface configured to connect a server with an input device and an output device of the vehicle. The server may store sample data, associated with the vehicle, that match a plurality of output responses corresponding respectively to the sample data. The server may generate, based on input data received, via the wireless interface from the input device of the vehicle, a sample datum from the stored sample data, retrieve, from the memory, an output response, of the plurality of output response, that matches the sample datum, output, via the wireless interface to the output device of the vehicle, the retrieved output response, perform multi-task learning based on the input data and the sample data, identify, based on the retrieved output response, a user intention, and cause, based on the identified user intention, the vehicle to be controlled.