Graph-to-Sequence Model for Semantic Parsing
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Solution Overview
Problem
Conventional neural semantic parsers neglect rich syntactic information such as dependency graphs and constituent parses, leading to incomplete representation of text meaning in logical forms.
Innovation Solution
A graph-to-sequence model is employed, generating a syntactic graph for a sentence, producing node embeddings based on connected nodes, and using attention-based RNN decoding to generate logical translations, incorporating sentence, dependency, and constituency features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a sequential encoder (SeqLSTM) is used to encode text, then the model architecture is simple and easy to implement, but it only extracts word order features and neglects useful syntactic information such as dependency parse and constituency parse
Solution Approach 1:
The patent combines multiple information sources (word sequence, dependency parse, constituency parse) into a unified graph-structured representation. The graph encoder integrates these different syntactic features by constructing a syntactic graph that incorporates both dependency relations and constituency structure, allowing the model to simultaneously leverage word order and hierarchical syntactic information without choosing one over the other
2Device complexity
If conventional sequence-to-sequence models are used for semantic parsing, then the model structure is straightforward, but they neglect rich syntactic information such as dependency graph and constituent parse
Solution Approach 1:
The patent transitions from a one-dimensional sequential representation to a multi-dimensional graph-structured representation. By organizing syntactic information into a graph with nodes and edges that capture both dependency relations and constituency hierarchy, the model adds structural dimensions that enable more accurate semantic parsing while maintaining a relatively clean architectural framework
3Measurement precision
If a graph encoder is introduced to capture syntactic information, then the representation accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent segments the encoding process into distinct components: a graph construction module that builds the syntactic graph from input text, a graph encoding module that processes the graph structure, and a sequence decoding module that generates output. This segmentation allows each component to specialize in handling specific aspects of the task, improving representation accuracy while keeping individual module complexities manageable
Data Source
AI summary
A semantic parsing method using a graph-to-sequence model, system, and computer program product include generating a syntactic graph for a sentence, generating node embeddings for each node based on other nodes the each node is connected to in the syntactic graph, generating a graph embedding over the node embeddings, performing attention-based recurrent neural network (RNN) decoding of the graph embedding and the node embeddings, and providing a logical translation of the sentence based on the decoding.