Introspective Generator for NLP Feature Selection
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Solution Overview
Problem
Current natural language processing (NLP) models face challenges in selecting relevant input features for prediction, leading to poor performance and lack of control over information outside the selected features, resulting in degeneration and sub-optimal communication between generator and predictor networks.
Innovation Solution
A three-player selective rationalization system is introduced, where a generator network selects input features, a first predictor network predicts an output label, and a complementary predictor network predicts a label based on unselected features, playing adversarial and minimax games to ensure comprehensive rationale extraction, with an introspective generator predicting labels before feature selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a generator network selects input features for prediction, then predictive accuracy is improved, but control over information outside selected features is lost leading to degeneration
Solution Approach 1:
A complement controller network is introduced as an intermediary between the generator network and the complement features. This mediator regulates which complement features are passed to the predictor, ensuring controlled information flow while maintaining predictive accuracy. The complement controller learns to select complementary features that provide useful information without causing degeneration.
Solution Approach 2:
The system implements feedback mechanisms where the predictor's performance on selected features and complement features is continuously monitored. This feedback guides the generator and complement controller to adjust feature selection dynamically, preventing degeneration by ensuring that complement features do not contain harmful or redundant information.
2Ease of operation
If selective rationalization is applied to expose relevant text portions, then interpretability is improved, but architectural constraints limit performance
Solution Approach 1:
The input features are segmented into two distinct portions: selected features that the generator chooses for prediction, and complement features that are excluded. This segmentation allows the system to maintain interpretability by exposing only relevant selected features, while the complement features are controlled to prevent information leakage that would compromise accuracy.
Solution Approach 2:
The system dynamically changes the parameters of feature selection by learning optimal selection strategies through training. The generator and complement controller adapt their feature selection behavior based on learned patterns, achieving both interpretability through selective exposure and accuracy through optimized feature choices.
3Ease of operation
If module networks are designed to expose reasoning mechanisms, then self-explanation capability is improved, but architectural complexity increases and accuracy lags behind blackbox approaches
Solution Approach 1:
The generator network serves multiple functions: it selects relevant features for prediction, generates explanations for the predictions, and implicitly identifies important patterns in the data. This multi-functionality achieves self-explanation capability without requiring separate specialized modules, thereby avoiding excessive architectural complexity while maintaining accuracy.
Data Source
AI summary
A method and system of training a natural language processing network are provided. A corpus of data is received and one or more input features selected therefrom by a generator network. The one or more selected input features from the generator network are received by a first predictor network and used to predict a first output label. A complement of the selected input features from the generator network are received by a second predictor network and used to predict a second output label.