Semantic Parsing Validity Determination via Alignment Scores
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for determining the validity of answers from semantic parsing models require creating separate data sets and designing/training additional models, leading to inconsistencies in answer possibility and model behavior.
Innovation Solution
A validity determination apparatus that acquires natural sentences, executes semantic parsing, calculates alignment scores, and determines the validity of semantic representations using a trained model without the need for additional data sets or model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a separate data set and additional model are created for answer possibility determination, then the validity determination capability is improved, but the device complexity and training requirements increase
Solution Approach 1:
The patent combines the answer possibility determination function with the existing semantic parsing model by adding an output layer that computes alignment scores between input and output tokens. This merging approach allows the model to perform both semantic parsing and validity determination using a single unified structure, eliminating the need for separate models while maintaining reliability.
Solution Approach 2:
The semantic parsing model is designed to serve multiple functions: it performs semantic parsing to generate output tokens and simultaneously computes alignment scores to determine answer possibility. This multi-functionality is achieved through the output layer that calculates alignment between input and output, allowing one model to fulfill multiple roles without requiring additional specialized models.
2Reliability
If an answer possibility model is created separately, then the validity determination is improved, but the inconsistency between answer possibility and semantic parsing increases
Solution Approach 1:
By merging the validity determination function into the semantic parsing model itself, the patent ensures that both semantic parsing and answer possibility determination share the same underlying representations and processing logic. This integration guarantees consistency between the two functions since they operate within the same model framework using shared hidden states and parameters.
Solution Approach 2:
The output layer computes alignment scores that provide feedback about the consistency between input and output tokens. This feedback mechanism allows the model to internally evaluate its own parsing results, ensuring that the answer possibility determination is aligned with the semantic parsing process rather than being an independent, potentially inconsistent assessment.
3Measurement precision
If additional data sets and model training are performed, then the validity determination accuracy is improved, but the loss of time and computational resources increases
Solution Approach 1:
The alignment score computation is built into the model's output layer structure, allowing validity determination to be performed as a natural byproduct of the semantic parsing process. Since the computational framework is preliminarily designed to support both functions simultaneously, no additional training time is required beyond what is already needed for semantic parsing.
Solution Approach 2:
The model performs its own validity determination using its internal representations and the alignment score computation built into its output layer. This self-service capability eliminates the need for separate validation models and their associated training requirements, as the semantic parsing model itself generates validity assessments during normal operation.
Data Source
AI summary
A validity determination apparatus according to one embodiment includes: circuitry configured to acquire a natural sentence; output a semantic representation by executing semantic parsing with the natural sentence as an input; calculate a first alignment score representing a relationship between input/output tokens of the natural sentence and the semantic representation; calculate a second alignment score of a word unit from a relationship between a token included in the input/output tokens and a word; extract natural sentence content words by executing part-of-speech parsing on the natural sentence; a grammar parser configured to extract semantic representation content words by executing grammar parsing on the semantic representation; and determine whether the semantic representation is valid on the basis of the second alignment score, the natural sentence content words, and the semantic representation content words.