Joint Many-Task Neural Network Model Using Successive Regularization
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Solution Overview
Problem
Traditional natural language processing (NLP) models fail to effectively leverage linguistic hierarchies and often suffer from catastrophic forgetting when trained on multiple tasks, leading to suboptimal performance in complex NLP tasks such as chunking, dependency parsing, semantic relatedness, and textual entailment.
Innovation Solution
A joint many-task neural network model is introduced, which is trained end-to-end for multiple NLP tasks using a stacked LSTM architecture with bypass connections and a successive regularization technique to prevent catastrophic interference, along with a joint-embedding method for robust encoding of words and dimensionality projection to reduce overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional NLP models are trained on multiple tasks, then task coverage increases, but catastrophic forgetting occurs leading to performance degradation
Solution Approach 1:
The patent applies successive regularization by预先 storing the old weight values before updating them during multi-task training. The regularization term λ||W - W_old||² constrains the weight changes to prevent catastrophic forgetting of previously learned tasks, allowing the model to maintain performance stability while covering multiple tasks
Solution Approach 2:
The patent modifies the training objective function by adding a regularization term that penalizes large deviations from previous weight configurations. This parameter change in the loss function (Equation 2) allows the model to adapt to new tasks while maintaining reliability on previously learned tasks through controlled parameter evolution
2Productivity
If a joint many-task neural network model is trained end-to-end, then learning efficiency improves, but overfitting increases
Solution Approach 1:
The patent applies dimensionality projection by transforming the high-dimensional output of the neural network through a projection matrix P to a lower-dimensional space. This parameter transformation (Equation 3: z = P h) reduces the model's capacity to memorize noise while preserving task-relevant features, thereby reducing overfitting and improving generalization accuracy
Solution Approach 2:
The patent introduces a dimensionality reduction step that projects the hidden state representations from the original feature space into a compressed representation space. This dimensional transformation maintains the essential task information while reducing the risk of overfitting by eliminating redundant or noisy dimensions
3Reliability
If linguistic hierarchies are leveraged in NLP models, then task performance improves, but model complexity increases
Solution Approach 1:
The patent implements a universal stacked LSTM architecture that processes multiple NLP tasks (POS tagging, chunking, dependency parsing, semantic relatedness, textual entailment) through a single unified model structure. This multi-functional design leverages linguistic hierarchies across all tasks without requiring separate specialized models, thereby improving task performance while managing complexity through architectural reuse
Solution Approach 2:
The patent segments the complex NLP processing into hierarchical layers (word level, phrase level, sentence level, paragraph level) where each layer focuses on specific linguistic phenomena. This segmentation allows the model to handle complex tasks by breaking them down into manageable sub-tasks processed at appropriate hierarchical levels, improving performance without overwhelming model complexity
Data Source
AI summary
The technology disclosed provides a so-called “joint many-task neural network model” to solve a variety of increasingly complex natural language processing (NLP) tasks using growing depth of layers in a single end-to-end model. The model is successively trained by considering linguistic hierarchies, directly connecting word representations to all model layers, explicitly using predictions in lower tasks, and applying a so-called “successive regularization” technique to prevent catastrophic forgetting. Three examples of lower level model layers are part-of-speech (POS) tagging layer, chunking layer, and dependency parsing layer. Two examples of higher level model layers are semantic relatedness layer and textual entailment layer. The model achieves the state-of-the-art results on chunking, dependency parsing, semantic relatedness and textual entailment.