Neural Symbolic Reader for Multi-Step Reasoning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current natural language processing systems for reading comprehension struggle with multi-step reasoning and domain scalability, particularly in unstructured data sources, due to limitations in integrating neural networks with symbolic reasoning and handling complex operations like counting and sorting.
Innovation Solution
The development of a Neural Symbolic Reader (NeRd) system that combines a machine-learned reader model with a programmer model to generate executable programs for natural language text passages, using domain-specific language and operators for multi-step reasoning, and employs data augmentation and hard Expectation-Maximization with thresholding for training with weak supervision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized neural modules are used for each question type, then reading comprehension accuracy is improved, but adaptability to multiple domains deteriorates
Solution Approach 1:
The patent implements a universal neural symbolic framework that can handle multiple question types and domains through a single architecture. The system uses a unified set of symbolic operators (span selection, arithmetic operations, counting, sorting) that can be composited to solve different types of reasoning problems, eliminating the need for separate specialized modules for each question type while maintaining high accuracy across domains.
2Speed
If neural networks are used for reading comprehension, then processing speed is improved, but capability for multi-step reasoning deteriorates
Solution Approach 1:
The patent introduces symbolic operators as intermediaries between the neural network and the reasoning process. The neural network generates embeddings and selects operators, but the actual multi-step reasoning is performed by executing composed symbolic operations (span selection, arithmetic, counting, sorting) on the text data. This intermediary symbolic layer enables complex multi-step reasoning while keeping the neural network component relatively simple and fast.
3Device complexity
If structured parsing is applied to unstructured text, then reasoning capability is improved, but error rate increases due to cascade errors
Solution Approach 1:
The patent segments the reasoning process into independent, composable symbolic operators rather than applying a monolithic structured parsing approach. Each operator (span selection, arithmetic operation, counting, sorting) is a discrete, well-defined unit that can be executed independently and composed in sequences. This segmentation eliminates cascade errors because each operator's output is directly verifiable and can be independently corrected without affecting other parts of the reasoning chain.
Data Source
AI summary
The present disclosure is directed to systems and methods for performing reading comprehension with machine learning. More specifically, the present disclosure is directed to a Neural Symbolic Reader (example implementations of which may be referred to as NeRd), which includes a reader to encode the passage and question, and a programmer to generate a program for multi-step reasoning. By using operators like span selection, the program can be executed over a natural language text passage to generate an answer to a natural language text question. NeRd is domain-agnostic such that the same neural architecture works for different domains. Further, NeRd is compositional such that complex programs can be generated by compositionally applying the symbolic operators.