Alternating Neural Attention for Machine Reading Comprehension
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Solution Overview
Problem
Current natural language processing systems face challenges in achieving near-human level comprehension of unstructured text, particularly in tasks like information extraction and machine translation, due to limitations in understanding and reasoning with natural language.
Innovation Solution
A natural language comprehension system (NLCS) employing an alternating attention mechanism, utilizing bi-directional recurrent neural networks with gated recurrent units, iteratively processes questions and text to produce query and text glimpses, allowing for sequential reasoning and efficient prediction of answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional natural language processing systems process entire questions and documents simultaneously, then they can capture all information, but they require excessive computation time and resources
Solution Approach 1:
The patent divides the question and document into multiple glimpses (segments) that are processed iteratively. At each time step, the system processes a portion of the question (query glimpse) and a corresponding portion of the document (text glimpse), then updates its understanding. This segmentation allows the system to process information in manageable chunks, significantly reducing computation time while maintaining the ability to capture relevant information through the iterative refinement of glimpses.
Solution Approach 2:
The system performs preliminary processing by generating initial glimpses of the question and document before full processing is complete. These preliminary glimpses provide early insights that guide subsequent processing steps, allowing the system to identify and focus on relevant portions of the text ahead of time, thereby improving overall processing efficiency without losing important information.
2Measurement precision
If the system processes all query and text glimpses iteratively to ensure accuracy, then answer precision improves, but computation time increases
Solution Approach 1:
The patent implements periodic action by processing glimpses in discrete time steps with a fixed number of iterations. The system alternates between processing query glimpses and text glimpses at each time step, performing a predetermined number of iterations (e.g., 3-5 steps). This periodic processing ensures that the system achieves sufficient accuracy through repeated refinement while preventing excessive computation time by limiting the total number of iterations.
Solution Approach 2:
The system performs partial processing by not requiring all possible glimpses to be fully processed to achieve satisfactory accuracy. Instead of exhaustively processing every possible combination of query and text portions, the system processes a sufficient number of glimpses to reach an acceptable level of answer accuracy, thereby avoiding unnecessary computation time while maintaining measurement precision.
3Adaptability or versatility
If the system uses detailed bi-directional recurrent neural networks with gated recurrent units, then understanding depth improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation to the neural network processing by dividing the comprehension task into separate specialized components: one network for processing query glimpses and another for processing text glimpses. Each network focuses on its specific function, which simplifies the overall system architecture compared to a single monolithic network that would need to handle all processing tasks. This segmentation maintains comprehension depth while reducing system complexity.
Solution Approach 2:
The bi-directional recurrent neural networks with gated recurrent units serve multiple functions: they process both query and text glimpses, maintain contextual information from previous time steps, and generate refined understandings at each iteration. This multi-functionality allows the system to achieve deep comprehension using standardized network components rather than requiring multiple specialized complex modules, thereby improving adaptability while managing system complexity.
Data Source
AI summary
Described herein are systems and methods for providing a natural language comprehension system (NLCS) that iteratively performs an alternating search to gather information that may be used to predict the answer to the question. The NLCS first attends to a query glimpse of the question, and then finds one or more corresponding matches by attending to a text glimpse of the text.