Semi-supervised Stacked Autoencoder for Sentence Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current deep learning-based methods for classification rely heavily on massive amounts of labeled data, which is costly, and semi-supervised approaches that use unlabeled data for learning word embeddings do not effectively leverage both unlabeled and labeled data jointly, limiting their application to natural language processing tasks, especially in sentence classification.
Innovation Solution
A semi-supervised model combining a residual stacked de-noising autoencoder (RSDA) with a supervised classifier, trained simultaneously with unlabeled and labeled data, to generate sentence embeddings that can be used for various classification tasks such as paraphrase identification and sentiment detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning-based methods use massive amounts of labeled data for classification, then classification performance is improved, but data acquisition cost increases
Solution Approach 1:
The patent merges supervised learning (using labeled data) and unsupervised learning (using unlabeled data) into a unified semi-supervised framework. The autoencoder processes unlabeled data to learn representations, while the classifier uses labeled data for supervised training, and both components work together in an integrated system that leverages the strengths of both approaches.
Solution Approach 2:
The autoencoder performs preliminary feature extraction and representation learning on unlabeled data before the classification stage. By pre-processing unlabeled data through the autoencoder to generate meaningful embeddings, the system prepares the data in advance for more effective supervised classification, reducing the reliance on large amounts of labeled data.
2Adaptability or versatility
If semi-supervised methods use unlabeled data for learning word embeddings in a pipeline setting, then data utilization is improved, but feature learning from both data types jointly is prevented
Solution Approach 1:
The patent merges the autoencoder and classifier into a unified architecture where both labeled and unlabeled data can be processed simultaneously. The loss function combines reconstruction loss from the autoencoder and classification loss from the classifier, enabling joint optimization that allows discriminatory features to be learned from both data types together rather than in separate pipeline stages.
3Ease of operation
If current semi-supervised methods focus on single sentence classification, then task simplicity is maintained, but application scope is limited
Solution Approach 1:
The patent creates a universal semi-supervised framework that can handle multiple NLP tasks beyond single sentence classification. The architecture is designed to be task-agnostic and can be applied to sentence classification, paraphrase identification, sentiment detection, and other text classification tasks by simply changing the label space and training data, making it highly versatile while maintaining operational simplicity.
Data Source
AI summary
Techniques described herein relate to semi-supervised training and application of stacked autoencoders and other classifiers for predictive and other purposes. In various embodiments, a semi-supervised model (108) may be trained for sentence classification, and may combine what is referred to herein as a “residual stacked de-noising autoencoder” (“RSDA”) (220), which may be unsupervised, with a supervised classifier (218) such as a classification neural network (e.g., a multilayer perceptron, or “MLP”). In various embodiments, the RSDA may be a stacked denoising autoencoder that may or may not include one or more residual connections. If present, the residual connections may help the RSDA “remember” forgotten information across multiple layers. In various embodiments, the semi-supervised model may be trained with unlabeled data (for the RSDA) and labeled data (for the classifier) simultaneously.