Multimodal Session Matching for Context-Aware Customer Service Responses
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Solution Overview
Problem
Existing intelligent customer service technologies based on natural language processing struggle to accurately understand user context and intention due to the diversification of user input, which often includes multi-modal information such as text and images, leading to reduced response accuracy and user experience.
Innovation Solution
A session response method that performs multi-modal semantic representation on both text and image features, using a multi-modal semantic representation model trained through contrastive learning, and integrates a session matching model with a context representation model to fuse and understand multiple modalities, enhancing the recognition of text structure and overall page layout, and utilizing a cascade search knowledge base for accurate response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single text understanding is used, then the system is simple and fast, but it cannot fully understand user context and intention leading to low response accuracy
Solution Approach 1:
The patent merges multiple modalities (text and image) into a unified multi-modal semantic representation system. The text encoding model and image encoding model process their respective inputs separately, then the feature fusion model integrates these representations to create a comprehensive understanding of user context, thereby improving response accuracy without creating separate independent systems.
Solution Approach 2:
The multi-modal semantic representation model serves multiple functions: it processes both text and image inputs, performs semantic understanding, extracts features, and generates responses. This universal approach allows a single system to handle diverse user inputs (text-only, image-only, or combined) while maintaining high response accuracy across different modalities.
2Measurement precision
If multi-modal information is processed, then user context understanding improves, but processing time and computational resources increase
Solution Approach 1:
The patent segments the processing pipeline into distinct modules: text encoding model for text input, image encoding model for image input, feature fusion model for integration, and response generation model for output. This segmentation allows each component to process its specific modality independently and efficiently, then combines results without requiring complete reprocessing of all data.
Solution Approach 2:
The system performs preliminary semantic representation and feature extraction on both text and image inputs before final response generation. By pre-processing and representing the semantic meaning of inputs in a standardized format, the system reduces the computational burden during the final integration and response generation stages, thereby decreasing overall processing time.
3Measurement precision
If multi-modal semantic representation is performed, then response accuracy improves, but model complexity and training requirements increase
Solution Approach 1:
The patent combines the text encoding model and image encoding model into a unified multi-modal semantic representation framework. Both models share common architectural components and training objectives, allowing them to process different modalities through a consistent lens. The feature fusion model then merges their outputs, creating a comprehensive representation that improves response accuracy while managing model complexity through shared structures.
Solution Approach 2:
The system adapts the encoding models to handle different modalities by adjusting input parameters and processing dimensions. The text encoding model processes textual sequences while the image encoding model processes visual data, but both are transformed into a common feature space through the feature fusion model. This parameter adaptation allows the system to maintain a unified model architecture while handling diverse input types.
Data Source
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AI summary
Provided are a session response method and apparatus, an electronic device and a storage medium. The method comprises: acquiring multi-modal session information arranged according to a session time sequence, wherein the multi-modal session information includes first modal information and second modal information (S110); performing multi-modal semantic representation on at least two modal features in the first modal information to obtain a first representation vector (S120); performing semantic representation of a corresponding modality on the second modal information to obtain a second representation vector (S130); inputting the first representation vector, the second representation vector and the corresponding session time sequence into a session matching model, respectively, to obtain a matching result (S140); and responding to the multi-modal session information according to the matching result (S150).