Hybrid Question Parser Executor Framework for Unstructured Text
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Solution Overview
Problem
Existing question answering systems face challenges in handling questions of varying complexity from unstructured text, particularly in defining logical forms and executing them effectively, due to the difficulty in parsing natural language questions and dealing with unstructured data.
Innovation Solution
The Hybrid Question Parser and Executor (HPE) framework combines neural network approaches with symbolic reasoning, parsing input questions into hybrid expressions (H-Expressions) that can be iteratively executed to generate answers, providing a flexible and interpretable solution for complex question answering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If semantic parsers are used to convert natural language questions to intermediate logical forms, then question answering capability is improved, but the difficulty of defining logical forms for textual questions increases
Solution Approach 1:
The patent introduces an intermediary execution model that acts as a mediator between the semantic parser and the unstructured text. The execution model translates intermediate logical forms into operations that can be executed on unstructured text, thereby resolving the difficulty of directly defining logical forms for textual questions while maintaining question answering capability
Solution Approach 2:
The system segments the question answering process into distinct components: a semantic parser that converts natural language to intermediate logical forms, and an execution model that handles the text-specific operations. This segmentation allows each component to be optimized independently, reducing the overall complexity of defining logical forms for textual questions
2Productivity
If execution is designed for structured data, then execution efficiency is improved, but the ability to handle unstructured text deteriorates
Solution Approach 1:
The execution model is designed to be dynamic and adaptable, allowing it to handle both structured and unstructured data types. The model can adjust its execution strategy based on the input data type, maintaining efficiency while gaining versatility to process unstructured text effectively
Solution Approach 2:
The execution model is designed with multi-functionality to handle various data types including both structured and unstructured text. By creating a universal execution framework that can adapt to different data formats, the system maintains execution efficiency while gaining the ability to process diverse unstructured text inputs
3Device complexity
If direct answer generation is used, then system simplicity is improved, but interpretability and error identification capability deteriorate
Solution Approach 1:
The system performs preliminary action by generating intermediate logical forms before producing the final answer. This intermediate representation preserves the reasoning process and structure, enabling better interpretability and error identification while maintaining relative system simplicity through the use of standardized intermediate forms
Data Source
AI summary
Embodiments described herein provide systems and methods for question answering using a hybrid question parser and executor model. The hybrid question parser and executor model includes a hybrid parser model and a hybrid executor model. The hybrid parser model includes a first neural network model, and generates a representation of an input question. The representation includes primitives and operations representing relationships among the primitives. The hybrid executor model generates an answer to the input question by executing the representation based on an input text document. The hybrid executor model includes an execution neural network model for executing the primitives of the representation, and an execution programming model for executing the operations of the representation.