Reading Comprehension Neural Network for Accurate Question Answering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current natural language processing systems face challenges in accurately answering complex questions from electronic documents without prior knowledge of language structure, and they require specific encoding of document or query structure.
Innovation Solution
A reading comprehension neural network system that uses a combination of reader and selection neural networks to process document and question tokens, generating a joint representation and token scores to determine the best answer, without requiring specific encoding of document or query structure, and can be trained on large-scale datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specific encoding of document or query structure is used, then processing accuracy is improved, but system complexity and prior knowledge requirements increase
Solution Approach 1:
The neural network performs self-service by automatically learning document and query structure representations during training without requiring manual encoding specifications. The network adapts to different document structures autonomously, eliminating the need for pre-defined encoding schemes while maintaining high answer accuracy.
Solution Approach 2:
The system changes parameters dynamically by adjusting neural network weights and biases during training to adapt to different document and query structures. This allows the model to handle varied structures without requiring explicit encoding specifications, resolving the contradiction between accuracy and complexity.
2Adaptability or versatility
If neural networks are trained with minimal prior knowledge of language structure, then adaptability is improved, but training data requirements and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-training on large corpora to learn general language structures before fine-tuning on specific question answering tasks. This preliminary learning reduces the amount of additional training data needed while maintaining high adaptability to different language structures.
Solution Approach 2:
The neural network is designed with universal architecture that can handle multiple language structures and document types. This multi-functionality allows the model to adapt to different structures without requiring separate training for each, reducing overall training data requirements while maintaining versatility.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting answers to questions about documents. One of the methods includes receiving a document comprising a plurality of document tokens; receiving a question associated with the document, the question comprising a plurality of question tokens; processing the document tokens and the question tokens using a reader neural network to generate a joint numeric representation of the document and the question; and selecting, from the plurality of document tokens, an answer to the question using the joint numeric representation of the document and the question.