Machine Reading Comprehension via Attention Vector Aggregation
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Solution Overview
Problem
Current machine reading comprehension methods are inefficient in rapidly processing articles to answer questions, lacking effective mechanisms for extracting relevant information and predicting answer positions within the text.
Innovation Solution
A method involving encoding question and article vectors, using bidirectional and shared attention models, self-attention mechanisms, and a bidirectional GRU network to aggregate and predict answer positions, simulating human-like comprehension by transforming symbols into vectors and fusing feature information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional machine reading comprehension methods are used to process articles and answer questions, then the system can provide answers, but the processing efficiency is low and it cannot rapidly comprehend articles
Solution Approach 1:
The patent segments the article processing into multiple stages: character-level encoding, word-level encoding, attention-based feature extraction, and answer prediction. This segmentation allows parallel processing of different aspects (question encoding, article encoding, attention calculation) which significantly improves processing efficiency and reduces comprehension time.
Solution Approach 2:
The patent performs preliminary encoding of both the article and question into vector representations before the actual comprehension process. This preliminary action transforms raw text into structured numerical formats that can be rapidly processed by the attention mechanisms, avoiding the need for repeated text parsing during the comprehension phase.
2Measurement precision
If simple encoding methods are used, then the processing is fast, but the semantic understanding and answer accuracy are insufficient
Solution Approach 1:
The patent introduces attention mechanisms as intermediaries between the encoded article and question vectors and the final answer prediction. The attention mechanism calculates relevance scores between question words and article segments, acting as a mediator that identifies the most important regions for answering, thereby improving accuracy without requiring overly complex model structures.
Solution Approach 2:
The patent transforms the text processing problem into a parameter optimization problem by converting text into vector representations and using learnable parameters in the attention mechanism and neural network layers. This allows the system to learn optimal transformations from training data, improving answer position prediction accuracy while maintaining a manageable model complexity through parameter sharing and efficient optimization.
Data Source
AI summary
A method for machine reading comprehension includes: S1, obtaining a character-level indication vector of a question and a character-level indication vector of an article; S2, obtaining an encoded question vector and an encoded article vector; S3, obtaining an output P1 of a bidirectional attention model and an output P2 of a shared attention model; S4, obtaining an aggregated vector P3; S5, obtaining a text encoding vector P4; S6, obtaining global interaction information between words within the article; S7, obtaining a text vector P5 after using the self-attention model; S8, obtaining aggregated data P6 according to the text encoding vector P4 and the text vector P5; S9, obtaining a context vector of the article according to the aggregated data P6 and an unencoded article vector P; and S10, predicting an answer position according to the context vector of the article and the encoded question vector to complete the machine reading comprehension.