Interpreting Deep Neural Dialogue Agents via Feature Interaction Analysis
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Solution Overview
Problem
Current dialogue agents using neural models operate in an opaque manner, making it difficult for human users to understand and trust the responses generated, as conventional interpretation techniques are model-specific and do not provide adequate interpretative cues when model states or parameters are unknown.
Innovation Solution
A system that includes a feature extraction component to extract features from a dialogue model independently, a model interpreter to analyze dialogue context by identifying pairwise interactions, and a feature constructor to generate visual representations, facilitating interpretation across multiple contexts and improving user trust and automation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If neural models are used for dialogue agents, then automation capability and response generation are improved, but interpretability and user trust deteriorate due to opaque operation
Solution Approach 1:
The patent introduces an interpretation module as an intermediary between the neural dialogue model and the user. This module extracts and visualizes key features and decision pathways from the model's opaque operations, providing a bridge that maintains automation capability while making the model's reasoning process visible and understandable to users.
Solution Approach 2:
The patent replaces the purely mechanical black-box neural model operation with a hybrid system that incorporates symbolic interpretation and visualization components. This substitution allows the system to maintain the computational power of neural networks while adding layers of interpretability through feature extraction and explanation generation.
2Measurement precision
If model-specific interpretation techniques are used, then interpretation accuracy for that model is improved, but adaptability to different models deteriorates
Solution Approach 1:
The patent designs an interpretation framework with universal components that can adapt to different neural dialogue models. The feature extraction module uses model-agnostic techniques to identify important features regardless of the specific model architecture, while the visualization component provides consistent interpretation outputs across different model types, achieving both accuracy and adaptability.
3Adaptability or versatility
If feature extraction is performed independently from the dialogue model, then adaptability to different models is improved, but interpretation precision may deteriorate
Solution Approach 1:
The independent feature extraction module employs self-service mechanisms by automatically adapting its extraction strategies based on the characteristics of the dialogue model it interfaces with. Through self-calibration and automated feature importance assessment, the system maintains high interpretation precision without requiring model-specific customization, achieving both independence and accuracy.
Data Source
AI summary
Techniques facilitating interpretation of deep neural model based dialogue agents are provided. In one example, a computer-implemented method comprises extracting, by a device operatively coupled to a processor, features from a dialogue model independently from the dialogue model, the features comprising input features provided to the dialogue model and output features produced via the dialogue model in response to the input features, resulting in extracted features; and analyzing, by the device, a dialogue context associated with the extracted features by identifying pairwise interactions between respective ones of the extracted features.