Bottom-Up Neural Semantic Parsing for Low-Overhead Logical Forms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural semantic parsers face challenges in achieving compositional generalization and efficient processing of natural language expressions, particularly in constructing logical forms that require significant computational resources and memory.
Innovation Solution
A bottom-up neural semantic parsing approach that builds tree structures incrementally, using a directed acyclic graph (DAG) representation and semi-autoregressive decoding to generate logical forms, minimizing computational overhead and memory usage while improving generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional neural semantic parsers construct logical forms using full autoregressive decoding, then they can generate complete semantic representations, but they require significant computational resources and memory
Solution Approach 1:
The patent segments the logical form generation process into multiple stages: first generating a simplified linear sequence using lightweight autoregressive decoding, then separately constructing the tree structure through bottom-up parsing. This segmentation allows each stage to use optimized algorithms appropriate to its specific task, reducing overall computational overhead while maintaining generation accuracy.
Solution Approach 2:
The patent performs preliminary action by first generating a linear sequence representation of the logical form before constructing the final tree structure. This preliminary linear sequence serves as a scaffold that guides subsequent tree construction, eliminating the need for complex real-time decisions during full autoregressive decoding and thereby reducing computational resources required.
2Adaptability or versatility
If traditional neural semantic parsers use full autoregressive decoding to construct tree structures, then they achieve compositional generalization, but they consume excessive memory resources
Solution Approach 1:
The patent inverts the traditional approach by first generating a linear sequence and then constructing the tree structure bottom-up from that sequence, rather than directly generating the tree structure through autoregressive decoding. This inversion maintains compositional generalization capabilities while significantly reducing memory consumption by processing information in a more efficient order.
Solution Approach 2:
The patent introduces dynamic adaptability by allowing the system to switch between different decoding strategies depending on the input complexity. For simple expressions, it uses the efficient linear sequence approach; for complex expressions requiring compositional generalization, it activates the bottom-up tree construction mechanism, optimizing memory usage across different scenarios.
3Loss of information
If neural semantic parsers process natural language expressions token by token in autoregressive manner, then they maintain context information, but they increase processing time
Solution Approach 1:
The patent segments context processing into two phases: first, a rapid linear sequence generation phase that captures global context efficiently; second, a targeted bottom-up tree construction phase that refines local context relationships. This segmentation reduces processing time by avoiding redundant context processing while maintaining essential context information.
Solution Approach 2:
The patent creates a simplified copy of the logical form as a linear sequence that preserves essential context information. This linear copy serves as a lightweight representation that can be processed quickly, while the original tree structure is reconstructed from it, thereby maintaining context without the full processing overhead of traditional autoregressive methods.
Data Source
AI summary
A decoder of a neural semantic parser receives input data associated with a natural language expression. An action is selected from a queue of actions, the queue of actions storing at least one action, the action being associated with an element from vocabulary of the natural language expression. The selected action is processed to build a tree structure where the processing of the selected action expands the tree structure with a node representing the element, where the tree structure is expanded bottom-up. A set of new actions is generated based on the node associated with the selected action and the vocabulary. The set of new actions is added to the queue of actions. The decoder repeats selecting, processing, generating and adding until a criterion is met. A logical form of the natural language expression is output based on the tree structure.