GAN Bag-of-Ngrams Model for Question Answering Accuracy
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Solution Overview
Problem
Current natural language processing techniques, such as statistical language models, rely on false independence assumptions and are limited in their ability to learn word co-occurrences and positions in text, making them less effective for tasks like question answering and text classification.
Innovation Solution
The use of Generative Adversarial Networks (GANs) to generate a bag-of-ngrams model, which learns word co-occurrences and positions jointly, allowing for the generation of a probability distribution over the full vocabulary and enabling effective natural language processing operations like question answering and text classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical language models are used for natural language processing, then the system can process text data, but the model relies on false independence assumptions and is limited in learning word co-occurrences and positions
Solution Approach 1:
The patent replaces traditional statistical language models with a Generative Adversarial Network (GAN) based system. The GAN uses neural network components (generator and discriminator) to learn word co-occurrences and positions jointly, substituting the mechanical statistical approach with a more sophisticated neural architecture that captures contextual relationships without independence assumptions.
Solution Approach 2:
The patent changes the fundamental parameters of the language model by transitioning from univariate word probability estimates in statistical models to multivariate joint probability distributions over words and positions in GANs. This parameter change enables the model to capture complex co-occurrence patterns and positional relationships simultaneously.
2Reliability
If Generative Adversarial Networks are used to generate bag-of-ngrams model, then the system can learn word co-occurrences and positions jointly, but the training process requires complex neural network architecture
Solution Approach 1:
The patent segments the complex GAN architecture into distinct functional components: a generator neural network that creates bag-of-ngrams representations, a discriminator that evaluates them, and a training mechanism that coordinates their interaction. This segmentation makes the complex system more manageable and trainable despite its overall complexity.
Solution Approach 2:
The patent introduces an intermediary bag-of-ngrams representation that bridges the input text and the neural network processing. This intermediary structure captures essential linguistic features (word co-occurrences and positions) in a compressed format, facilitating more efficient training and reducing the direct complexity burden on the neural networks.
3Productivity
If traditional natural language processing methods are used, then the system requires large amounts of natural language data for training, but the GAN-based mechanism can be trained quickly and efficiently with less data
Solution Approach 1:
The patent applies preliminary action by pre-training the GAN on general language patterns and bag-of-ngrams representations before fine-tuning for specific NLP tasks. This preliminary training phase enables the model to learn fundamental linguistic structures efficiently, reducing the amount of task-specific data needed and accelerating overall training speed.
Data Source
AI summary
Mechanisms are provided for implementing a Question Answering (QA) system utilizing a trained generator of a generative adversarial network (GAN) that generates a bag-of-ngrams (BoN) output representing unlabeled data for performing a natural language processing operation. The QA system obtains a plurality of candidate answers to a natural language question, where each candidate answer comprises one or more ngrams. For each candidate answer, a confidence score is generated based on a comparison of the one or more ngrams in the candidate answer to ngrams in the BoN output of the generator neural network of the GAN. A final answer to the input natural language question is selected from the plurality of candidate answers based on the confidence scores associated with the candidate answers, and is output.