Dual Sequence Inference Neural Network Model
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Solution Overview
Problem
Current state-of-the-art question answering (QA) models achieve less than 72.3% exact match accuracy and less than 80.7% F1 score accuracy on datasets like SQuAD, indicating a need for improved neural network models that can achieve higher accuracy and require less training data and time.
Innovation Solution
The development of a neural network model for dual sequence inference, specifically a deep coattention encoder with residual connections, which processes pairs of input sequences (such as text documents and questions) to generate a codependent representation, facilitating more accurate inference and overcoming training challenges like vanishing gradients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a neural network model is trained using traditional supervised learning alone, then the model can achieve reasonable accuracy, but the training process is slow and requires a large amount of training data and time
Solution Approach 1:
The patent combines supervised learning and reinforcement learning into a unified training framework. The supervised learning component provides accurate label-based training while the reinforcement learning component optimizes the policy through reward signals. This merging allows the model to achieve both high accuracy and efficient training by leveraging the strengths of both paradigms: supervised learning provides a strong initial foundation and reinforcement learning refines the model's decision-making capabilities through iterative policy optimization.
Solution Approach 2:
The reinforcement learning component introduces a feedback mechanism where the model receives reward signals based on its predictions and gradually improves its policy. This feedback loop allows the model to learn from its mistakes and converge toward optimal solutions more efficiently than traditional supervised learning alone, accelerating the training process while maintaining high accuracy through continuous policy refinement.
2Reliability
If a single-layer coattention encoder is used, then the model structure is simple, but the accuracy is limited to less than 72.3% exact match and less than 80.7% F1 score
Solution Approach 1:
The encoder is divided into multiple layers, with each layer performing a specific function in the coattention mechanism. The first layer computes initial coattention weights and the second layer refines these weights through additional coattention computation. This segmentation allows the model to capture increasingly refined interactions between the question and document sequences, achieving higher accuracy (75.1% exact match, 83.1% F1 score) by breaking down the complex attention computation into manageable sequential steps.
3Reliability
If deep coattention encoding is applied to capture complex interactions, then accuracy improves, but training challenges like vanishing gradients occur
Solution Approach 1:
The reinforcement learning component provides feedback signals that help mitigate the vanishing gradient problem in deep coattention networks. By introducing reward-based feedback loops, the training process receives continuous signals about policy effectiveness, which helps propagate gradients more effectively through the deep encoder layers. This feedback mechanism makes the training of deep coattention models more stable and easier to optimize compared to traditional supervised learning alone.
Data Source
AI summary
A computer-implemented method for dual sequence inference using a neural network model includes generating a codependent representation based on a first input representation of a first sequence and a second input representation of a second sequence using an encoder of the neural network model and generating an inference based on the codependent representation using a decoder of the neural network model. The neural network model includes a plurality of model parameters learned according to a machine learning process. The encoder includes a plurality of coattention layers arranged sequentially, each coattention layer being configured to receive a pair of layer input representations and generate one or more summary representations, and an output layer configured to receive the one or more summary representations from a last layer among the plurality of coattention layers and generate the codependent representation.